Connected Speech in Neurodegenerative Language Disorders: A Review

Multimodal speech biomarkers for remote monitoring of ALS disease progression

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that severely impacts affected persons' speech and motor functions, yet early detection and tracking of disease progression remain challenging. The current gold standard for monitoring ALS progression, the ALS functional rating scale - revised (ALSFRS-R), is based on subjective ratings of symptom severity, and may not capture subtle but clinically meaningful changes due to a lack of granularity. Multimodal speech measures which can be automatically collected from patients in a remote fashion allow us to bridge this gap because they are continuous-valued and therefore, potentially more granular at capturing disease progression. Here we investigate the responsiveness and sensitivity of multimodal speech measures in persons with ALS (pALS) collected via a remote patient monitoring platform in an effort to quantify how long it takes to detect a clinically-meaningful change associated with disease progression. We recorded audio and video from 278 participants and automatically extracted multimodal speech biomarkers (acoustic, orofacial, linguistic) from the data. We find that the timing alignment of pALS speech relative to a canonical elicitation of the same prompt and the number of words used to describe a picture are the most responsive measures at detecting such change in both pALS with bulbar (n = 36) and non-bulbar onset (n = 107). Interestingly, the responsiveness of these measures is stable even at small sample sizes. We further found that certain speech measures are sensitive enough to track bulbar decline even when there is no patient-reported clinical change, i.e. the ALSFRS-R speech score remains unchanged at 3 out of a total possible score of 4. The findings of this study have the potential to facilitate improved, accelerated and cost-effective clinical trials and care.

Age-related changes in connected speech production: evidence from eye-tracking in the culturally adapted picture description task

Purpose

Age-related changes in connected speech production remain a subject of debate, yielding inconsistent findings across various tasks and measures. This study aimed to investigate the effects of aging on picture description tasks using two types of pictures: a standardized picture (the Beach picture) and a culturally and linguistically modified picture tailored for Korean speakers (the Han River picture).

Method

Twenty-four young adults and 22 older adults participated in two picture description tasks while their eye movements were recorded. Word-level linguistic variables were used to assess informativeness (Correct Information Units per minute) and productivity (noun and verb counts per utterance) of connected speech production. Eye-movement measures were employed to evaluate real-time cognitive processing associated with planning connected speech (pre-speech fixation counts and durations; eye fixations before the speech onset of each utterance).

Results and conclusions

The findings revealed age-related declines in linguistic measures, with older adults exhibiting decreased CIUs per minute and smaller counts of nouns and verbs per utterance. Age-related changes in eye movement measures were evident in that older adults displayed longer pre-speech fixation durations. Unlike younger adults, older adults exhibited higher pre-speech fixation counts on the Han River picture compared to the Beach picture, suggesting cognitive challenges in performing the task that requires producing more words and detailed descriptions. These results suggest that aging is associated with reduced informativeness and productivity of connected speech, as well as a decline in cognitive processing efficiency.

High frequency post-pause word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment

Background

Alzheimer's disease (AD) is characterized by progressive cognitive decline, including impairments in speech production and fluency. Mild cognitive impairment (MCI), a prodrome of AD, has also been linked with changes in speech behavior but to a more subtle degree.

Objective

This study aimed to investigate whether speech behavior immediately following both filled and unfilled pauses (post-pause speech behavior) differs between individuals with MCI and healthy controls (HCs), and how these differences are influenced by the cognitive demands of various speech tasks.

Methods

Transcribed speech samples were analyzed from both groups across different tasks, including immediate and delayed narrative recall, picture descriptions, and free responses. Key metrics including lexical and syntactic complexity, lexical frequency and diversity, and part of speech usage, both overall and post-pause, were examined.

Results

Significant differences in pause usage were observed between groups, with a higher incidence and longer latencies following these pauses in the MCI group. Lexical frequency following filled pauses was higher among MCI participants in the free response task but not in other tasks, potentially due to the relative cognitive load of the tasks. The immediate recall task was most useful at differentiating between groups. Predictive analyses utilizing random forest classifiers demonstrated high specificity in using speech behavior metrics to differentiate between MCI and HCs.

Conclusions

Speech behavior following pauses differs between MCI participants and healthy controls, with these differences being influenced by the cognitive demands of the speech tasks. These post-pause speech metrics can be easily integrated into existing speech analysis paradigms.

Responsible development of clinical speech AI: Bridging the gap between clinical research and technology

This perspective article explores the challenges and potential of using speech as a biomarker in clinical settings, particularly when constrained by the small clinical datasets typically available in such contexts. We contend that by integrating insights from speech science and clinical research, we can reduce sample complexity in clinical speech AI models with the potential to decrease timelines to translation. Most existing models are based on high-dimensional feature representations trained with limited sample sizes and often do not leverage insights from speech science and clinical research. This approach can lead to overfitting, where the models perform exceptionally well on training data but fail to generalize to new, unseen data. Additionally, without incorporating theoretical knowledge, these models may lack interpretability and robustness, making them challenging to troubleshoot or improve post-deployment. We propose a framework for organizing health conditions based on their impact on speech and promote the use of speech analytics in diverse clinical contexts beyond cross-sectional classification. For high-stakes clinical use cases, we advocate for a focus on explainable and individually-validated measures and stress the importance of rigorous validation frameworks and ethical considerations for responsible deployment. Bridging the gap between AI research and clinical speech research presents new opportunities for more efficient translation of speech-based AI tools and advancement of scientific discoveries in this interdisciplinary space, particularly if limited to small or retrospective datasets.

Dysfluency in primary progressive aphasia: Temporal speech parameters

Analysing spontaneous speech in individuals experiencing fluency difficulties holds potential for diagnosing speech and language disorders, including Primary Progressive Aphasia (PPA). Dysfluency in the spontaneous speech of patients with PPA has mostly been described in terms of abnormal pausing behaviour, but the temporal features related to speech have drawn little attention. This study compares speech-related fluency parameters in the three main variants of PPA and in typical speech. Forty-three adults participated in this research, thirteen with the logopenic variant of PPA (lvPPA), ten with the non-fluent variant (nfvPPA), nine with the semantic variant (svPPA), and eleven who were healthy age-matched adults. Participants' fluency was assessed through a picture description task from which 42 parameters were computed including syllable duration, speaking pace, the duration of speech chunks (i.e. interpausal units, IPU), and the number of linguistic units per IPU and per second. The results showed that each PPA variant exhibited abnormal speech characteristics reflecting various underlying factors, from motor speech deficits to higher-level issues. Out of the 42 parameters considered, 37 proved useful for characterising dysfluency in the three main PPA variants and 35 in distinguishing among them. Therefore, taking into account not only pausing behaviour but also temporal speech parameters can provide a fuller understanding of dysfluency in PPA. However, no single parameter by itself sufficed to distinguish one PPA group from the other two, further evidence that dysfluency is not dichotomous but rather multidimensional, and that complementary multiparametric analyses are needed.

Digital remote assessment of speech acoustics in cognitively unimpaired adults: feasibility, reliability and associations with amyloid pathology

BACKGROUND

Digital speech assessment has potential relevance in the earliest, preclinical stages of Alzheimer's disease (AD). We evaluated the feasibility, test-retest reliability, and association with AD-related amyloid-beta (Aβ) pathology of speech acoustics measured over multiple assessments in a remote setting.

METHODS

Fifty cognitively unimpaired adults (Age 68 ± 6.2 years, 58% female, 46% Aβ-positive) completed remote, tablet-based speech assessments (i.e., picture description, journal-prompt storytelling, verbal fluency tasks) for five days. The testing paradigm was repeated after 2-3 weeks. Acoustic speech features were automatically extracted from the voice recordings, and mean scores were calculated over the 5-day period. We assessed feasibility by adherence rates and usability ratings on the System Usability Scale (SUS) questionnaire. Test-retest reliability was examined with intraclass correlation coefficients (ICCs). We investigated the associations between acoustic features and Aβ-pathology, using linear regression models, adjusted for age, sex and education.

RESULTS

The speech assessment was feasible, indicated by 91.6% adherence and usability scores of 86.0 ± 9.9. High reliability (ICC ≥ 0.75) was found across averaged speech samples. Aβ-positive individuals displayed a higher pause-to-word ratio in picture description (B = -0.05, p = 0.040) and journal-prompt storytelling (B = -0.07, p = 0.032) than Aβ-negative individuals, although this effect lost significance after correction for multiple testing.

CONCLUSION

Our findings support the feasibility and reliability of multi-day remote assessment of speech acoustics in cognitively unimpaired individuals with and without Aβ-pathology, which lays the foundation for the use of speech biomarkers in the context of early AD.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models

INTRODUCTION

Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.

METHODS

We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases.

RESULTS

Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years.

DISCUSSION

The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment.

HIGHLIGHTS

Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Multimodal Technologies for Remote Assessment of Neurological and Mental Health

PURPOSE

Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale.

APPROACH

We first present an overview of the various modalities of health information-speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural-which can each be extracted from various signal sources-audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment.

FINDINGS

We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform.

CONCLUSION

This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation.

Gesture's Role in the Communication of Adults With Different Types of Aphasia

PURPOSE

Adults with aphasia gesture more than adults without aphasia. However, less is known about the role of gesture in different discourse contexts for individuals with different types of aphasia. In this study, we asked whether patterns of speech and gesture production of individuals with aphasia vary by aphasia and discourse type and also differ from the speech and gestures produced by adults without aphasia.

METHOD

We compared the amount, diversity, and complexity of speech and gesture production in adults with anomic or Broca's aphasia and adults with no aphasia (n = 20/group) in their first- versus third-person narratives.

RESULTS

Adults with Broca's aphasia showed the lowest performance in their amount, diversity, and complexity of speech production, followed by adults with anomic aphasia and adults without aphasia. This pattern was reversed for gesture production. Speech and gesture production also varied by discourse context. Adults with either type of aphasia used a lower amount of and less diverse speech in third-person than in first-person narratives; this pattern was also reversed for gesture production.

CONCLUSIONS

Overall, our results provide evidence for a compensatory role of gesture in aphasia communication. Adults with Broca's aphasia, who showed the greatest speech production difficulties, also relied most on gesture, and this pattern was particularly pronounced in the third-person narrative context.

Assessment of dementia knowledge in Indian speech-language pathology students

OBJECTIVES

Speech-language pathologists (SLPs) have a crucial role in assisting individuals with dementia due to the communication and swallowing challenges associated with the disease. As the number of dementia cases rises in India at an increasing rate, investigating the level of dementia knowledge of SLP students can offer insight into the preparedness of the healthcare system to meet this emerging demand.

METHOD

A cross-sectional survey was conducted on SLP students pursuing their final year undergraduate, postgraduate and doctoral degrees from four universities across India. Dementia knowledge was assessed using the Dementia Knowledge Assessment Scale (DKAS) and information about previous dementia exposure (both formal and informal) was collected. The collected data were analysed using quantitative methods.

RESULTS

A total of 220 students (64.70% response rate) completed the survey. Overall dementia knowledge was inadequate with an average score of 22.08 ± 10.06. Previous dementia exposure among the students was also found to be low and did not affect dementia knowledge scores.

DISCUSSION

Despite the fundamental role SLPs play in the care of individuals with dementia, the lack of knowledge in this area emphasizes the need for enhancing dementia training programs through educational curricula and clinical placements.

Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia

Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer's disease (AD) compromises the processing of entities expressed by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective with increased third-person references. Yet, no study has examined whether these patterns can be captured in connected speech via natural language processing tools. To tackle such gaps, we asked 96 participants (32 AD patients, 32 bvFTD patients, 32 healthy controls) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers (via part-of-speech and morphological tagging). We also extracted objective properties (frequency, phonological neighborhood, length, semantic variability) from each content word. In our main study (with 21 AD patients, 21 bvFTD patients, and 21 healthy controls), we used inferential statistics and machine learning for group-level and subject-level discrimination. The above linguistic features were correlated with patients' scores in tests of general cognitive status and executive functions. We found that, compared with HCs, (i) AD (but not bvFTD) patients produced significantly fewer nouns, (ii) bvFTD (but not AD) patients used significantly more third-person markers, and (iii) both patient groups produced more frequent words. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.71). A generalizability test, with a model trained on the entire main study sample and tested on hold-out samples (11 AD patients, 11 bvFTD patients, 11 healthy controls), showed even better performance, with AUCs of 0.76 and 0.83 for AD and bvFTD, respectively. No linguistic feature was significantly correlated with cognitive test scores in either patient group. These results suggest that specific cognitive traits of each disorder can be captured automatically in connected speech, favoring interpretability for enhanced syndrome characterization, diagnosis, and monitoring.

A Methodological Approach to Quantifying Silent Pauses, Speech Rate, and Articulation Rate across Distinct Narrative Tasks: Introducing the Connected Speech Analysis Protocol (CSAP)

The examination of connected speech may serve as a valuable tool for exploring speech output in both healthy speakers and individuals with language disorders. Numerous studies incorporate various fluency and silence measures into their analyses to investigate speech output patterns in different populations, along with the underlying cognitive processes that occur while speaking. However, methodological inconsistencies across existing studies pose challenges in comparing their results. In the current study, we introduce CSAP (Connected Speech Analysis Protocol), which is a specific methodological approach to investigate fluency metrics, such as articulation rate and speech rate, as well as silence measures, including silent pauses' frequency and duration. We emphasize the importance of employing a comprehensive set of measures within a specific methodological framework to better understand speech output patterns. Additionally, we advocate for the use of distinct narrative tasks for a thorough investigation of speech output in different conditions. We provide an example of data on which we implement CSAP to showcase the proposed pipeline. In conclusion, CSAP offers a comprehensive framework for investigating speech output patterns, incorporating fluency metrics and silence measures in distinct narrative tasks, thus allowing a detailed quantification of connected speech in both healthy and clinical populations. We emphasize the significance of adopting a unified methodological approach in connected speech studies, enabling the integration of results for more robust and generalizable conclusions.

Using explainable artificial intelligence to identify linguistic biomarkers of amyloid pathology in primary progressive aphasia

Introduction

Recent success has been achieved in Alzheimer's disease (AD) clinical trials targeting amyloid beta (β), demonstrating a reduction in the rate of cognitive decline. However, testing methods for amyloid-β positivity are currently costly or invasive, motivating the development of accessible screening approaches to steer patients toward appropriate diagnostic tests. Here, we employ a pre-trained language model (Distil-RoBERTa) to identify amyloid-β positivity from a short, connected speech sample. We further use explainable AI (XAI) methods to extract interpretable linguistic features that can be employed in clinical practice.

Methods

We obtained language samples from 74 patients with primary progressive aphasia (PPA) across its three variants. Amyloid-β positivity was established through the analysis of cerebrospinal fluid, amyloid PET, or autopsy. 51% of the sample was amyloid-positive. We trained Distil-RoBERTa for 16 epochs with a batch size of 6 and a learning rate of 5e-5, and used the LIME algorithm to train interpretation models to interpret the trained classifier's inference conditions.

Results

Over ten runs of 10-fold cross-validation, the classifier achieved a mean accuracy of 92%, SD = 0.01. Interpretation models were able to capture the classifier's behavior well, achieving an accuracy of 97% against classifier predictions, and uncovering several novel speech patterns that may characterize amyloid-β positivity.

Discussion

Our work improves previous research which indicates connected speech is a useful diagnostic input for prediction of the presence of amyloid-β in patients with PPA. Further, we leverage XAI techniques to reveal novel linguistic features that can be tested in clinical practice in the appropriate subspecialty setting. Computational linguistic analysis of connected speech shows great promise as a novel assessment method in patients with AD and related disorders.

Distinguishable features of spontaneous speech in Alzheimer's clinical syndrome and healthy controls

There is growing evidence that subtle changes in spontaneous speech may reflect early pathological changes in cognitive function. Recent work has found that lexical-semantic features of spontaneous speech predict cognitive dysfunction in individuals with mild cognitive impairment (MCI). The current study assessed whether Ostrand and Gunstad's (OG) lexical-semantic features extend to predicting cognitive status in a sample of individuals with Alzheimer's clinical syndrome (ACS) and healthy controls. Four additional (New) speech indices shown to be important in language processing research were also explored in this sample to extend prior work. Speech transcripts of the Cookie Theft Task from 81 individuals with ACS (Mage = 72.7 years, SD = 8.80, 70.4% female) and 61 healthy controls (HC) (Mage = 63.9 years, SD = 8.52, 62.3% female) from Dementia Bank were analyzed. Random forest and logistic machine learning techniques examined whether subject-level lexical-semantic features could be used to accurately discriminate those with ACS from HC. Results showed that logistic models with the New lexical-semantic features obtained good classification accuracy (78.4%), but the OG features had wider success across machine learning model types. In terms of sensitivity and specificity, the random forest model trained on the OG features was the most balanced. Findings from the current study suggest that features of spontaneous speech used to predict MCI may also distinguish between individuals with ACS and healthy controls. Future work should evaluate these lexical-semantic features in pre-clinical persons to further explore their potential to assist with early detection through speech analysis.

The Telephone Language Screener (TLS): standardization of a novel telephone-based screening test for language impairment

BACKGROUND

This study aimed at developing and standardizing the Telephone Language Screener (TLS), a novel, disease-nonspecific, telephone-based screening test for language disorders.

METHODS

The TLS was developed in strict pursuance to the current psycholinguistic standards. It comprises nine tasks assessing phonological, lexical-semantic and morpho-syntactic components, as well as an extra Backward Digit Span task. The TLS was administered to 480 healthy participants (HPs), along with the Telephone-based Semantic Verbal Fluency (t-SVF) test and a Telephone-based Composite Language Index (TBCLI), as well as to 37 cerebrovascular/neurodegenerative patients-who also underwent the language subscale of the Telephone Interview for Cognitive Status (TICS-L). An HP subsample was also administered an in-person language battery. Construct validity, factorial structure, internal consistency, test-retest and inter-rater reliability were tested. Norms were derived via Equivalent Scores. The capability of the TLS to discriminate patients from HPs and to identify, among the patient cohort, those with a defective TICS-L, was also examined.

RESULTS

The TLS was underpinned by a mono-component structure and converged with the t-SVF (p < .001), the TBCLI (p < .001) and the in-person language battery (p = .002). It was internally consistent (McDonald's ω = 0.67) and reliable between raters (ICC = 0.99) and at retest (ICC = 0.83). Age and education, but not sex, were predictors of TLS scores. The TLS optimally discriminated patients from HPs (AUC = 0.80) and successfully identified patients with an impaired TICS-L (AUC = 0.92). In patients, the TLS converged with TICS-L scores (p = 0.016).

DISCUSSION

The TLS is a valid, reliable, normed and clinically feasible telephone-based screener for language impairment.

Automatic classification of AD pathology in FTD phenotypes using natural speech

INTRODUCTION

Screening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech-based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD).

METHODS

We trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC = 36, FTLD = 60, healthy controls [HC] = 89). Patients' pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network-based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features.

RESULTS

Our classifier showed 0.88 ± $ \pm $ 0.03 area under the curve (AUC) for ADNC versus FTLD and 0.93 ± $ \pm $ 0.04 AUC for patients versus HC. Noun frequency and pause rate correlated with gray matter volume loss in the limbic and salience networks, respectively.

DISCUSSION

Brief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD.

HIGHLIGHTS

We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech-based neuropathology screening tool.

Investigating silent pauses in connected speech: integrating linguistic, neuropsychological, and neuroanatomical perspectives across narrative tasks in post-stroke aphasia

Introduction

Silent pauses are regarded as integral components of the temporal organization of speech. However, it has also been hypothesized that they serve as markers for internal cognitive processes, including word access, monitoring, planning, and memory functions. Although existing evidence across various pathological populations underscores the importance of investigating silent pauses' characteristics, particularly in terms of frequency and duration, there is a scarcity of data within the domain of post-stroke aphasia.

Methods

The primary objective of the present study is to scrutinize the frequency and duration of silent pauses in two distinct narrative tasks within a cohort of 32 patients with chronic post-stroke aphasia, in comparison with a control group of healthy speakers. Subsequently, we investigate potential correlation patterns between silent pause measures, i.e., frequency and duration, across the two narrative tasks within the patient group, their performance in neuropsychological assessments, and lesion data.

Results

Our findings showed that patients exhibited a higher frequency of longer-duration pauses in both narrative tasks compared to healthy speakers. Furthermore, within-group comparisons revealed that patients tended to pause more frequently and for longer durations in the picture description task, while healthy participants exhibited the opposite trend. With regard to our second research question, a marginally significant interaction emerged between performance in semantic verbal fluency and the narrative task, in relation to the location of silent pauses-whether between or within clauses-predicting the duration of silent pauses in the patient group. However, no significant results were observed for the frequency of silent pauses. Lastly, our study identified that the duration of silent pauses could be predicted by distinct Regions of Interest (ROIs) in spared tissue within the left hemisphere, as a function of the narrative task.

Discussion

Overall, this study follows an integrative approach of linguistic, neuropsychological and neuroanatomical data to define silent pauses in connected speech, and illustrates interrelations between cognitive components, temporal aspects of speech, and anatomical indices, while it further highlights the importance of studying connected speech indices using different narrative tasks.

Reliability of the picture description task of the Western Aphasia Battery - revised in Laurentian French persons without brain injury

Objective: Limited normative data (including psychometric properties) are currently available on discourse tasks in non-dominant languages such as Laurentian (Quebec) French. The lack of linguistic and cultural adaptation has been identified as a barrier to discourse assessment. The main aim of this study is to document inter-rater and test-retest reliability properties of the picnic scene of the Western Aphasia Battery - Revised (WAB-R), including the cultural adaptation of an information content unit (ICU) list, and provide a normative reference for persons without brain injury (PWBI). Method: To do so, we also aimed to adapt an ICU checklist culturally and linguistically for Laurentian French speakers. Discourse samples were collected from 66 PWBI using the picture description task of the WAB-R. The ICU list was first adapted into Laurentian French. Then, ICUs and thematic units (TUs) were extracted manually, and microstructural variables were extracted using CLAN. Inter-rater reliability and test-retest reliability were determined. Results: Excellent inter-rater reliability was obtained for ICUs and TUs, as well as for all microstructural variables, except for mean length of utterance, which was found to be good. Conversely, test-retest reliability ranged from poor to moderate for all variables. Conclusion: The present study provides a validated ICU checklist for clinicians and researchers working with Laurentian French speakers when assessing discourse with the picnic scene of the WAB-R. It also addresses the gap in available psychometric data regarding inter-rater and test-retest reliability in PWBI.

Universal and Language-Specific Connected Speech Characteristics of Bilingual Speakers With Alzheimer's Disease: Insights From Case Studies of Structurally Distinct Languages

PURPOSE

Connected speech analysis has been effectively utilized for the diagnosis and disease monitoring of individuals with Alzheimer's disease (AD). Existing research has been conducted mostly in monolingual English speakers with a noticeable lack of evidence from bilinguals and non-English speakers, particularly in non-European languages. Using a case study approach, we characterized connected speech profiles of two Bengali-English bilingual speakers with AD to determine the universal features of language impairments in both languages, identify language-specific differences between the languages, and explore language impairment characteristics of the participants with AD in relation to their bilingual language experience.

METHOD

Participants included two Bengali-English bilingual speakers with AD and a group of age-, gender-, education-, and language-matched neurologically healthy controls. Connected speech samples were collected in first language (L1; Bengali) and second language (L2; English) using a novel storytelling task (i.e., Frog, Where Are You?). These samples were analyzed using an augmented quantitative production analysis and correct information unit analyses for productivity, fluency, syntactic and morphosyntactic features, and lexical and semantic characteristics.

RESULTS

Irrespective of the language, AD impacted speech productivity (speech rate and fluency) and semantic characteristics in both languages. Unique language-specific differences were noted on syntactic measures (reduced sentence length in Bengali), lexical distribution (fewer pronouns and absence of reduplication in Bengali), and inflectional properties (no difficulties with noun or verb inflections in Bengali). Among the two participants with AD, the individual who showed lower proficiency and usage in L2 (English) demonstrated reduced syntactic complexity and morphosyntactic richness in English.

CONCLUSIONS

Evidence from these case studies suggests that language impairment features in AD are not universal across languages, particularly in comparison to impairments typically associated with language breakdowns in English. This study underscores the importance of establishing connected speech profiles in AD for non-English-speaking populations, especially for structurally different languages. This would in turn lead to the development of language-specific markers that can facilitate early detection of language deterioration and aid in improving diagnosis of AD in individuals belonging to underserved linguistically diverse populations.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25412458.

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Objective

Early detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.

Method

Data were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic "F" fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.

Results

Mean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.

Conclusion

Findings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

Disfluencies in spontaneous speech in persons with low-grade glioma before and after surgery

Impaired lexical retrieval is common in persons with low-grade glioma (LGG). Several studies have reported a discrepancy between subjective word-finding difficulties and results on formal tests. Analysis of spontaneous speech might be more sensitive to signs of word-finding difficulties, hence we aimed to explore disfluencies in a spontaneous-speech task performed by participants with presumed LGG before and after surgery. Further, we wanted to explore how the presence of disfluencies in spontaneous speech differed in the participants with and without objectively established lexical-retrieval impairment and with and without self-reported subjective experience of impaired language, speech and communication. Speech samples of 26 persons with presumed low-grade glioma were analysed with regard to disfluency features. The post-operative speech samples had a higher occurrence of fillers, implying more disfluent language production. The participants performed worse on two of the word fluency tests, and after surgery the number of participants who were assessed as having an impaired lexical retrieval had increased from 6 to 12. The number of participants who experienced a change in their language, speech or communication had increased from 9 to 12. Additional comparisons showed that those with impaired lexical retrieval had a higher proportion of false starts after surgery than those with normal lexical retrieval, and differences in articulation rate and speech rate, favouring those not having experienced any change in language, speech or communication. Taken together, the findings from this study strengthen the existing claim that temporal aspects of language and speech are important when assessing persons with gliomas.

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech

Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.

Longitudinal observational cohort study: Speech for Intelligent cognition change tracking and DEtection of Alzheimer's Disease (SIDE-AD)

INTRODUCTION

There is emerging evidence that speech may be a potential indicator and manifestation of early Alzheimer's disease (AD) pathology. Therefore, the University of Edinburgh and Sony Research have partnered to create the Speech for Intelligent cognition change tracking and DEtection of Alzheimer's Disease (SIDE-AD) study, which aims to develop digital speech-based biomarkers for use in neurodegenerative disease.

METHODS AND ANALYSIS

SIDE-AD is an observational longitudinal study, collecting samples of spontaneous speech. Participants are recruited from existing cohort studies as well as from the National Health Service (NHS)memory clinics in Scotland. Using an online platform, participants record a voice sample talking about their brain health and rate their mood, anxiety and apathy. The speech biomarkers will be analysed longitudinally, and we will use machine learning and natural language processing technology to automate the assessment of the respondents' speech patterns.

ETHICS AND DISSEMINATION

The SIDE-AD study has been approved by the NHS Research Ethics Committee (REC reference: 23/WM/0153, protocol number AC23046, IRAS Project ID 323311) and received NHS management approvals from Lothian, Fife and Forth Valley NHS boards. Our main ethical considerations pertain to the remote administration of the study, such as taking remote consent. To address this, we implemented a consent process, whereby the first step of the consent is done entirely remotely but a member of the research team contacts the participant over the phone to consent participants to the optional, most sensitive, elements of the study. Results will be presented at conferences, published in peer-reviewed journals and communicated to study participants.

Linguistic changes in neurodegenerative diseases relate to clinical symptoms

Background

The detection and characterization of speech changes may help in the identification and monitoring of neurodegenerative diseases. However, there is limited research validating the relationship between speech changes and clinical symptoms across a wide range of neurodegenerative diseases.

Method

We analyzed speech recordings from 109 patients who were diagnosed with various neurodegenerative diseases, including Alzheimer's disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 speech features, including the acoustics of speech and linguistic properties of spoken language. We investigated the relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Mattis Dementia Rating Scale (DRS), Western Aphasia Battery (WAB), and Boston Naming Task (BNT) in a heterogeneous patient population.

Result

Linguistic features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE and DRS scores were associated with the use of shorter words and fewer prepositional phrases. Increased impairment on WAB and BNT was correlated with the use of fewer nouns but more pronouns. Patients also differed from healthy adults as their speech duration was significantly shorter with more pauses.

Conclusion

Linguistic changes such as the use of simpler vocabularies and syntax were detectable in patients with different neurodegenerative diseases and correlated with cognitive decline. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification

In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

Heterogeneity and overlap in the continuum of linguistic profile of logopenic and semantic variants of primary progressive aphasia: a Profile Analysis based on Multidimensional Scaling study

BACKGROUND

Primary progressive aphasia (PPA) diagnostic criteria underestimate the complex presentation of semantic (sv) and logopenic (lv) variants, in which symptoms partially overlap, and mixed clinical presentation (mixed-PPA) and heterogenous profile (lvPPA +) are frequent. Conceptualization of similarities and differences of these clinical conditions is still scarce.

METHODS

Lexical, semantic, phonological, and working memory errors from nine language tasks of sixty-seven PPA were analyzed using Profile Analysis based on Multidimensional Scaling, which allowed us to create a distributed representation of patients' linguistic performance in a shared space. Patients had been studied with [18F] FDG-PET. Correlations were performed between metabolic and behavioral data.

RESULTS

Patients' profiles were distributed across a continuum. All PPA, but two, presented a lexical retrieval impairment, in terms of reduced production of verbs and nouns. svPPA patients occupied a fairly clumped space along the continuum, showing a preponderant semantic deficit, which correlated to fusiform gyrus hypometabolism, while only few presented working memory deficits. Adjacently, lvPPA + presented a semantic impairment combined with phonological deficits, which correlated with metabolism in the anterior fusiform gyrus and posterior middle temporal gyrus. Starting from the shared phonological deficit side, a large portion of the space was occupied by all lvPPA, showing a combination of phonological, lexical, and working memory deficits, with the latter correlating with posterior temporo-parietal hypometabolism. Mixed PPA did not show unique profile, distributing across the space.

DISCUSSION

Different clinical PPA entities exist but overlaps are frequent. Identifying shared and unique clinical markers is critical for research and clinical practice. Further research is needed to identify the role of genetic and pathological factors in such distribution, including also higher sample size of less represented groups.

The analysis of gestural and verbal pragmatic markers produced by Mild Cognitive Impaired participants during longitudinal and autobiographical interviews

CONTEXT

This corpus-based study presents a multimodal analysis of verbal pragmatic markers and non-verbal pragmatic markers in elderly people with Mild Cognitive Impairment aged over 75 years.

METHODS

The corpus collection and analysis methodology has been described in the Belgian CorpAGEst transversal study and the French VintAGE longitudinal and transversal oriented pilot studies. The protocols are available online in both English and French.

RESULTS & CONCLUSION

Our general findings indicate that with ageing, verbal pragmatic markers acquire an interactive function that allows people with MCI to maintain intersubjective relationships with their interlocutor. Furthermore, at the non-verbal level, gestural manifestations are increasingly used over time with a preference for non-verbal pragmatic markers with a referential function and an adaptive function. We aim to show the benefits of linguistic and interactional scientific investigation methods through cognitive impaired ageing for clinicians and family caregivers.

Spoken Language Alterations can Predict Phenoconversion in Isolated Rapid Eye Movement Sleep Behavior Disorder: A Multicenter Study

OBJECTIVE

This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD).

METHODS

Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years.

RESULTS

Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17).

INTERPRETATION

Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.

Evaluating the Diagnostic Potential of Connected Speech for Benign Laryngeal Disease Using Deep Learning Analysis

OBJECTIVES

This study aimed to evaluate the performance of artificial intelligence (AI) models using connected speech and vowel sounds in detecting benign laryngeal diseases.

STUDY DESIGN

Retrospective.

METHODS

Voice samples from 772 patients, including 502 with normal voices and 270 with vocal cord polyps, cysts, or nodules, were analyzed. We employed deep learning architectures, including convolutional neural networks (CNNs) and time series models, to process the speech data. The primary endpoint was the area under the receiver's operating characteristic curve for binary classification.

RESULTS

CNN models analyzing speech segments significantly outperformed those using vowel sounds in distinguishing patients with and without benign laryngeal diseases. The best-performing CNN model achieved areas under the receiver operating characteristic curve of 0.895 and 0.845 for speech and vowel sounds, respectively. Correlations between AI-generated disease probabilities and perceptual assessments were more pronounced in the connected-speech analyses. However, the time series models performed worse than the CNNs.

CONCLUSION

Connected speech analysis is more effective than traditional vowel sound analysis for the diagnosis of laryngeal voice disorders. This study highlights the potential of AI technologies in enhancing the diagnostic capabilities of speech, advocating further exploration, and validation in this field.

Connected speech features in non-English speakers with Alzheimer's disease: protocol for scoping review

BACKGROUND

A large body of literature indicates that connected speech profiles in patients with Alzheimer's disease (AD) can be utilized for diagnosis, disease monitoring, and for developing communication strategies for patients. Most connected speech research has been conducted in English, with little work in some European languages. Therefore, significant drawback remains with respect to the diversity of languages studied, and how the fragmentation of linguistic features differs across languages in AD. Accordingly, existing reviews on connected speech in AD have focused on findings from English-speaking patients; none have specifically focused on the linguistic diversity of AD populations. This scoping review is undertaken to provide the currently reported characteristics of connected speech in AD in languages other than English. It also seeks to identify the type of assessments, methods to elicit speech samples, type of analysis and linguistic frameworks used, and micro- and macro-linguistic features of speech reported in non-English speakers with AD.

METHOD

We will conduct a scoping review of published studies that have quantitively assessed connected speech in AD in languages other than English. The inclusion criteria for the studies would be subject/s with a clinical diagnosis of AD. The search will include the electronic databases PubMed, Ovid-Embase, PsycINFO, Linguistic and Language Behaviour Abstracts (LLBA), and Web of Science up until March 2023. Findings will be mapped and described according to the languages studied, the methodology employed (e.g., patient characteristics, tasks used, linguistic analysis framework utilized), and connected speech profiles derived (e.g., micro- and macro-linguistic reported).

DISCUSSION

The scoping review will provide an overview of languages studied in connected speech research in AD with variation in linguistic features across languages, thus allowing comparison with the established key features that distinguish AD patients from healthy controls. The findings will inform future research in connected speech in different languages to facilitate robust connected speech research in linguistically and ethnically diverse populations.

Turkish adaptation, reliability, and validity of the detection test for language impairments in adults and the aged

There is no quick, valid, and reliable screening tool in Turkish that can be used for screening language disorders associated with major neurocognitive disorders (MND). To fill this gap, we designed three distinct studies. In the first study, we adapted the Detection Test for Language Impairments in Adults and the Aged into the Turkish language (DTLA-Tr). In the second study, we collected data from 175 Turkish individuals to determine the normative data of the DTLA-Tr. In the last study, we investigated the psychometric properties of the DLTA-Tr by comparing 17 healthy individuals with 17 patients with Alzheimer's disease and determining its test-retest reliability. As a result of Study 1, the DTLA was adapted to the Turkish adult population. In Study 2, the normative data of the DTLA-Tr were provided. The results of this study indicated a positive correlation between educational level and DTLA-Tr total score. The results of Study 3 showed that the DTLA-Tr has high predictive validity and good test-retest reliability. The DTLA-Tr is a valid and reliable tool for assessing language abilities in both adults and the elderly. The findings of this study have significant implications for the evaluation of language in Turkish-speaking patients with MND.

Natural language processing techniques for studying language in pathological ageing: A scoping review

BACKGROUND

In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria).

AIMS

To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach.

METHODS & PROCEDURES

We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews.

MAIN CONTRIBUTION

The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders).

CONCLUSIONS & IMPLICATIONS

Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.

WHAT THIS PAPER ADDS

What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

Assessing the Linguistic Capacity Across Alzheimer's Disease and Its Preclinical Stages: Evidence from Narrative Macrostructure in Elderly Speakers of Greek

Background

The assessment of language deficits can be valuable in the early clinical diagnosis of neurodegenerative disorders, including Alzheimer's disease (AD).

Objective

The present study aims to explore whether language markers at the macrostructural level could assist with the placement of an individual across the dementia continuum employing production data from structured narratives.

Methods

We administered a Picture Sequence Narrative Discourse Task to 170 speakers of Greek: young healthy controls (yHC), cognitively intact healthy elders (eHC), elder participants with subjective cognitive impairment (SCI), with mild cognitive impairment (MCI), and with AD dementia at the mild/moderate stages. Structural MRIs, medical history, neurological examination, and neuropsychological/cognitive screening determined the status of each speaker to appropriately groupthem.

Results

The data analysis revealed that the Macrostructure Index, Irrelevant Info, and Narration Density markers can track cognitive decline and AD (p < 0.001; Macrostructural Index: eHC versus AD Sensitivity 93.8%, Specificity 74.4%, MCI versus AD Sensitivity 93.8%, Specificity 66.7%; Narration Density: eHC versus AD Sensitivity 90.6%, Specificity 71.8%, MCI versus AD Sensitivity 93.8%, Specificity 66.7%). Moreover, Narrative Complexity was significantly affected for subjects with AD, Irrelevant Info increased in the narrations of speakers with MCI and AD, while Narration Length did not appear to indubitably differentiate between the cognitively intact groups and the clinical ones.

Conclusions

Narrative Macrostructure Indices provide valuable information on the language profile of speakers with(out) intact cognition revealing subtle early signs of cognitive decline and AD suggesting that the inclusion of language-based assessment tools would facilitate the clinical process.

Methodologies for assessing morphosyntactic ability in people with Alzheimer's disease

BACKGROUND

The detection and description of language impairments in neurodegenerative diseases like Alzheimer's Disease (AD) play an important role in research, clinical diagnosis and intervention. Various methodological protocols have been implemented for the assessment of morphosyntactic abilities in AD; narrative discourse elicitation tasks and structured experimental tasks for production, offline and online structured experimental tasks for comprehension. Very few studies implement and compare different methodological protocols; thus, little is known about the advantages and disadvantages of each methodology.

AIMS

To discuss and compare the main behavioral methodological approaches and tasks that have been used in psycholinguistic research to assess different aspects of morphosyntactic production and comprehension in individuals with AD at the word and sentence levels.

METHODS

A narrative review was conducted through searches in the scientific databases Google Scholar, Scopus, Science Direct, MITCogNet, PubMed. Only studies written in English, that reported quantitative data and were published in peer-reviewed journals were considered with respect to their methodological protocol. Moreover, we considered studies that reported research on all stages of the disease and we included only studies that also reported results of a healthy control group. Studies that implemented standardized assessment tools were not considered in this review.

OUTCOMES & RESULTS

The main narrative discourse elicitation tasks implemented for the assessment of morphosyntactic production include interviews, picture-description and story narration, whereas the main structured experimental tasks include sentence completion, constrained sentence production, sentence repetition and naming. Morphosyntactic comprehension in AD has been assessed with the use of structured experimental tasks, both offline (sentence-picture matching, grammaticality judgment) and online (cross-modal naming,speeded sentence acceptability judgment, auditory moving window, word detection, reading). For each task we considered studies that reported results from different morphosyntactic structures and phenomena in as many different languages as possible.

CONCLUSIONS & IMPLICATIONS

Our review revealed strengths and weaknesses of these methods but also directions for future research. Narrative discourse elicitation tasks as well as structured experimental tasks have been used in a variety of languages, and have uncovered preserved morphosyntactic production but also deficits in people with AD. A combination of narrative discourse elicitation and structured production tasks for the assessment of the same morphosyntactic structure has been rarely used. Regarding comprehension, offline tasks have been implemented in various languages, whereas online tasks have been mainly used in English. Offline and online experimental paradigms have often produced contradictory results even within the same study. The discrepancy between the two paradigms has been attributed to the different working memory demands they impose to the comprehender or to the different parsing processes they tap. Strengths and shortcomings of each methodology are summarized in the paper, and comparisons between different tasks are attempted when this is possible. Thus, the paper may serve as a methodological guide for the study of morphosyntax in AD and possibly in other neurodegenerative diseases.

WHAT THIS PAPER ADDS

What is already known on this subject For the assessment of morphosyntactic abilities in AD, various methodological paradigms have been implemented: narrative discourse elicitation tasks and structured experimental tasks for production, and offline and online structured experimental tasks for comprehension. Very few studies implement and compare different methodological protocols; thus, little is known about the advantages and disadvantages of each methodology. What this paper adds to existing knowledge The paper presents an overview of methodologies that have been used to assess morphosyntactic production and comprehension of people with AD at the word and sentence levels. The paper summarizes the strengths and shortcomings of each methodology, providing both the researcher and the clinician with some directions in their endeavour of investigating language in AD. Also, the paper highlights the need for further research that will implement carefully scrutinized tasks from various experimental paradigms and will explore distinct aspects of the AD patients' morphosyntactic abilities in typologically different languages. What are the potential or actual clinical implications of this work? The paper may serve as a reference point for (psycho-)linguists who wish to study morphosyntactic abilities in AD, and for speech and language therapists who might need to apply morphosyntactic protocols to their patients in order to assess them or design appropriate therapeutic interventions for production and comprehension deficits.

Phase I trial of the MuSic to CONnect (MuSiCON) protocol: feasibility and effect of choir participation for individuals with cognitive impairment

BACKGROUND

Individuals living in residential aged care facilities with cognitive decline are at risk of social isolation and decreased wellbeing. These risks may be exacerbated by decline in communication skills. There is growing awareness that group singing may improve sense of wellbeing for individuals with dementia. However, to date few studies have examined broader rehabilitative effects on skills such as communication of individuals with dementia.

AIMS

To determine the feasibility and acceptability of the MuSic to Connect (MuSiCON) choir and language/communication assessment protocol in people with cognitive impairment living in non-high-care wards of a residential facility.

METHODS

Six individuals with mild-moderate cognitive impairment participated (age range 55-91 years, five female, one male). A mixed method approach was used. Quantitative outcomes included attendance rates, quality of life and communication measures. The qualitative measure was a brief survey of experience completed by participants and carers post-intervention.

RESULTS

Overall, MuSiCON was perceived as positive and beneficial, with high attendance, perception of improved daily functioning and high therapeutic benefit without harmful effects. While there was no reliable change in communication skills over the course of the six-week intervention, most participants successfully engaged in the conversational task, suggesting it is a suitable and ecologically valid method for data collection.

CONCLUSIONS

The MuSiCON protocol demonstrated feasibility and was well received by participants and staff at the residential facility. A co-design approach is recommended to improve upon feasibility, acceptability and validity of the assessment protocol prior to Phase II testing.

Speech and language markers of neurodegeneration: a call for global equity

In the field of neurodegeneration, speech and language assessments are useful for diagnosing aphasic syndromes and for characterizing other disorders. As a complement to classic tests, scalable and low-cost digital tools can capture relevant anomalies automatically, potentially supporting the quest for globally equitable markers of brain health. However, this promise remains unfulfilled due to limited linguistic diversity in scientific works and clinical instruments. Here we argue for cross-linguistic research as a core strategy to counter this problem. First, we survey the contributions of linguistic assessments in the study of primary progressive aphasia and the three most prevalent neurodegenerative disorders worldwide-Alzheimer's disease, Parkinson's disease, and behavioural variant frontotemporal dementia. Second, we address two forms of linguistic unfairness in the literature: the neglect of most of the world's 7000 languages and the preponderance of English-speaking cohorts. Third, we review studies showing that linguistic dysfunctions in a given disorder may vary depending on the patient's language and that English speakers offer a suboptimal benchmark for other language groups. Finally, we highlight different approaches, tools and initiatives for cross-linguistic research, identifying core challenges for their deployment. Overall, we seek to inspire timely actions to counter a looming source of inequity in behavioural neurology.

Applying core lexicon analysis in patients with anomic aphasia: Based on Mandarin AphasiaBank

BACKGROUND

Patients with anomic aphasia experience difficulties in narrative processing. General discourse measures are time consuming and require necessary skills. Core lexicon analysis has been proposed as an effort-saving approach but has not been developed in Mandarin discourse.

AIMS

This exploratory study was aimed (1) to apply core lexicon analysis in Mandarin patients with anomic aphasia at the discourse level and (2) to verify the problems with core words among people with anomic aphasia.

METHODS & PROCEDURE

The core nouns and verbs were extracted from narrative language samples from 88 healthy participants. The production of core words for 12 anomic aphasia and 12 age- and education-matched controls were then calculated and compared. The correlation between the percentages and the Aphasia Quotients of the revised Western Aphasia Battery was analyzed as well.

OUTCOMES & RESULTS

The core nouns and verbs were successfully extracted. Patients with anomic aphasia produced fewer core words than healthy people, and the percentages differed significantly in different tasks as well as word classes. There was no correlation between the core lexicon use and the severity of aphasia in patients with anomic aphasia.

CONCLUSIONS & IMPLICATIONS

Core lexicon analysis may potentially serve as a clinician-friendly manner of quantifying core words produced at the discourse level in Mandarin patients with anomic aphasia.

WHAT THIS PAPER ADDS

What is already known on the subject Discourse analyses in aphasia assessment and treatment have increasingly garnered attention. Core lexicon analysis based on English AphasiaBank has been reported in recent years. It is correlated with microlinguistic and macrolinguistic measures in aphasia narratives. Nevertheless, the application based on Mandarin AphasiaBank is still under development in healthy individuals and patients with anomic aphasia. What this paper adds to existing knowledge A Mandarin core lexicon set was developed for different tasks. The feasibility of core lexicon analysis to evaluate the corpus of patients with anomic aphasia was preliminarily discussed and the speech performance of patients and healthy people was then compared to provide a reference for the evaluation and treatment of clinical aphasia corpus. What are the potential or actual clinical implications of this work? The purpose of this exploratory study was to consider the potential use of core lexicon analysis to evaluate core word production in narrative discourse. Moreover, normative and aphasia data were provided for comparison to develop clinical use for Mandarin patients with anomic aphasia.

Remote data collection speech analysis in people at risk for Alzheimer's disease dementia: usability and acceptability results

Introduction

Digital cognitive assessments are gathering importance for the decentralized remote clinical trials of the future. Before including such assessments in clinical trials, they must be tested to confirm feasibility and acceptability with the intended participant group. This study presents usability and acceptability data from the Speech on the Phone Assessment (SPeAk) study.

Methods

Participants (N = 68, mean age 70.43 years, 52.9% male) provided demographic data and completed baseline and 3-month follow-up phone based assessments. The baseline visit was administered by a trained researcher and included a spontaneous speech assessment and a brief cognitive battery (immediate and delayed recall, digit span, and verbal fluency). The follow-up visit repeated the cognitive battery which was administered by an automatic phone bot. Participants were randomized to receive their cognitive test results acer the final or acer each study visit. Participants completed acceptability questionnaires electronically acer each study visit.

Results

There was excellent retention (98.5%), few technical issues (n = 5), and good interrater reliability. Participants rated the assessment as acceptable, confirming the ease of use of the technology and their comfort in completing cognitive tasks on the phone. Participants generally reported feeling happy to receive the results of their cognitive tests, and this disclosure did not cause participants to feel worried.

Discussion

The results from this usability and acceptability analysis suggest that completing this brief battery of cognitive tests via a telephone call is both acceptable and feasible in a midlife-to-older adult population in the United Kingdom, living at risk for Alzheimer's disease.

Lexical Speech Features of Spontaneous Speech in Older Persons With and Without Cognitive Impairment: Reliability Analysis

Background

Speech analysis data are promising digital biomarkers for the early detection of Alzheimer disease. However, despite its importance, very few studies in this area have examined whether older adults produce spontaneous speech with characteristics that are sufficiently consistent to be used as proxy markers of cognitive status.

Objective

This preliminary study seeks to investigate consistency across lexical characteristics of speech in older adults with and without cognitive impairment.

Methods

A total of 39 older adults from a larger, ongoing study (age: mean 81.1, SD 5.9 years) were included. Participants completed neuropsychological testing and both picture description tasks and expository tasks to elicit speech. Participants with T-scores of ≤40 on ≥2 cognitive tests were categorized as having mild cognitive impairment (MCI). Speech features were computed automatically by using Python and the Natural Language Toolkit.

Results

Reliability indices based on mean correlations for picture description tasks and expository tasks were similar in persons with and without MCI (with r ranging from 0.49 to 0.65 within tasks). Intraindividual variability was generally preserved across lexical speech features. Speech rate and filler rate were the most consistent indices for the cognitively intact group, and speech rate was the most consistent for the MCI group.

Conclusions

Our findings suggest that automatically calculated lexical properties of speech are consistent in older adults with varying levels of cognitive impairment. These findings encourage further investigation of the utility of speech analysis and other digital biomarkers for monitoring cognitive status over time.

Further evaluation of narrative description as a measure of cognitive function in Alzheimer's disease

OBJECTIVE

The narrative description (ND) test objectively measures the ability to understand and describe visual scenes. As subtle differences in speech occur early in cognitive decline, we analyzed linguistic features for their utility in detecting cognitive impairment and predicting downstream decline.

METHOD

Participants (n = 52) with normal cognition to mild dementia performed the ND test (watched twenty 30-s video clips and described the visual content). Cognitive function was followed for up to 5 years. We computed simple linguistic features such as content efficiency, speech rate, and part of speech and unique word counts. We examined (a) relationships between cognitive status and ND score and linguistic features; (b) ability to discriminate early cognitive impairment from normal cognition using ND score and linguistic features; and (c) whether ND score and linguistic features were associated with future cognitive functional decline.

RESULTS

Many of the linguistic-feature metrics were related to cognitive status. Many of the linguistic features could distinguish between the cognitively normal group and the mild cognitive impairment (MCI) and Dementia groups. The area under the curve (AUC) for ND score alone was 0.74, with a nonsignificant increase to 0.78 when adding mean word length. Among participants with subjective cognitive decline (SCD) at the first visit, a smaller number of words plus more interjections or a lower ND score at baseline were predictive of future cognitive decline.

CONCLUSIONS

While many linguistic features were associated with cognitive status, and some were able to detect early cognitive impairment or predictive of future cognitive decline, all the features we tested seem to have been captured by the ND score. Thus, adding linguistic measures to the ND test score did not add to its value in assessing current or predicting future cognitive status. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.

Novel Screening Tool Using Non-linguistic Voice Features Derived from Simple Phrases to Detect Mild Cognitive Impairment and Dementia

Appropriate intervention and care in detecting cognitive impairment early are essential to effectively prevent the progression of cognitive deterioration. Diagnostic voice analysis is a noninvasive and inexpensive screening method that could be useful for detecting cognitive deterioration at earlier stages such as mild cognitive impairment. We aimed to distinguish between patients with dementia or mild cognitive impairment and healthy controls by using purely acoustic features (i.e., nonlinguistic features) extracted from two simple phrases. Voice was analyzed on 195 recordings from 150 patients (age, 45-95 years). We applied a machine learning algorithm (LightGBM; Microsoft, Redmond, WA, USA) to test whether the healthy control, mild cognitive impairment, and dementia groups could be accurately classified, based on acoustic features. Our algorithm performed well: area under the curve was 0.81 and accuracy, 66.7% for the 3-class classification. Thus, our vocal biomarker is useful for automated assistance in diagnosing early cognitive deterioration.

Differences in Connected Speech Outcomes Across Elicitation Methods

Background

Connected speech is often used to assess many aspects of an individual's language abilities after stroke. However, it is unknown the degree to which elicitation methods differ in generating structural and syntactic aspects of connected speech, two critical components of successful communication. Quantifying the degree to which elicitation methods differ in eliciting structurally, syntactically, and lexically complex connected speech at the earliest stage of stroke before reorganization and rehabilitation of function independent of clinical diagnosis of aphasia has not been examined to date. Addressing this gap has implications for early clinical intervention as well as empirical studies of connected speech production.

Aims

We compared two common elicitation methods, picture description and storytelling on lexical, structural, and syntactic measures of connected speech in speakers during the acute stage of left hemisphere stroke.

Methods & Procedures

We measured connected speech using an automated quantitative production analysis approach (Fromm et al., 2021) in 71 native-English speaking participants (27 female; 59 ± 13 years) within an average 3.9 days from left hemisphere stroke onset. We tested the degree of agreement and consistency between elicitation methods for lexical, structural, and syntactic measures of connected speech, as well as the degree of concordance in classifying deficits across individuals.

Outcomes & Results

Storytelling elicited significantly more words and more structurally complex, lexically diverse, and syntactically accurate speech in comparison to picture description. Elicitation methods differed in measuring outcomes across participants for the lexical and syntactic, but not structural complexity aspects of connected speech where storytelling classified more participants with impairments in comparison to picture description.

Conclusions

These differences suggest storytelling provides assessment of connected speech abilities more reflective of real-world abilities where its use is particularly critical for examining individual differences and providing diagnoses of acute stroke language deficits. As a result, using storytelling as a connected speech elicitation method more effectively captures a patient's language capabilities after stroke, consequently informing clinical diagnosis and treatment.

Combining Multiple Multimodal Speech Features into an Interpretable Index Score for Capturing Disease Progression in Amyotrophic Lateral Sclerosis

Multiple speech biomarkers have been shown to carry useful information regarding Amyotrophic Lateral Sclerosis (ALS) pathology. We propose a two-step framework to compute optimal linear combinations (indexes) of these biomarkers that are more discriminative and noise-robust than the individual markers, which is important for clinical care and pharmaceutical trial applications. First, we use a hierarchical clustering based method to select representative speech metrics from a dataset comprising 143 people with ALS and 135 age- and sex-matched healthy controls. Second, we analyze three methods of index computation that optimize linear discriminability, Youden Index, and sparsity of logistic regression model weights, respectively, and evaluate their performance with 5-fold cross validation. We find that the proposed indexes are generally more discriminative of bulbar vs non-bulbar onset in ALS than their individual component metrics as well as an equally-weighted baseline.

PROSA-a multicenter prospective observational study to develop low-burden digital speech biomarkers in ALS and FTD

Objective: There is a need for novel biomarkers that can indicate disease state, project disease progression, or assess response to treatment for amyotrophic lateral sclerosis (ALS) and associated neurodegenerative diseases such as frontotemporal dementia (FTD). Digital biomarkers are especially promising as they can be collected non-invasively and at low burden for patients. Speech biomarkers have the potential to objectively measure cognitive, motor as well as respiratory symptoms at low-cost and in a remote fashion using widely available technology such as telephone calls. Methods: The PROSA study aims to develop and evaluate low-burden frequent prognostic digital speech biomarkers. The main goal is to create a single, easy-to-perform battery that serves as a valid and reliable proxy for cognitive, respiratory, and motor domains in ALS and FTD. The study will be a multicenter 12-months observational study aiming to include 75 ALS and 75 FTD patients as well as 50 healthy controls and build on three established longitudinal cohorts: DANCER, DESCRIBE-ALS and DESCRIBE-FTD. In addition to the extensive clinical phenotyping in DESCRIBE, PROSA collects a comprehensive speech protocol in fully remote and automated fashion over the telephone at four time points. This longitudinal speech data, together with gold standard measures, will allow advanced speech analysis using artificial intelligence for the development of speech-based phenotypes of ALS and FTD patients measuring cognitive, motor and respiratory symptoms. Conclusion: Speech-based phenotypes can be used to develop diagnostic and prognostic models predicting clinical change. Results are expected to have implications for future clinical trial stratification as well as supporting innovative trial designs in ALS and FTD.

Explainable Alzheimer's Disease Detection Using Linguistic Features from Automatic Speech Recognition

INTRODUCTION

Alzheimer's disease (AD) is the most prevalent type of dementia and can cause abnormal cognitive function and progressive loss of essential life skills. Early screening is thus necessary for the prevention and intervention of AD. Speech dysfunction is an early onset symptom of AD patients. Recent studies have demonstrated the promise of automated acoustic assessment using acoustic or linguistic features extracted from speech. However, most previous studies have relied on manual transcription of text to extract linguistic features, which weakens the efficiency of automated assessment. The present study thus investigates the effectiveness of automatic speech recognition (ASR) in building an end-to-end automated speech analysis model for AD detection.

METHODS

We implemented three publicly available ASR engines and compared the classification performance using the ADReSS-IS2020 dataset. Besides, the SHapley Additive exPlanations algorithm was then used to identify critical features that contributed most to model performance.

RESULTS

Three automatic transcription tools obtained mean word error rate texts of 32%, 43%, and 40%, respectively. These automated texts achieved similar or even better results than manual texts in model performance for detecting dementia, achieving classification accuracies of 89.58%, 83.33%, and 81.25%, respectively.

CONCLUSION

Our best model, using ensemble learning, is comparable to the state-of-the-art manual transcription-based methods, suggesting the possibility of an end-to-end medical assistance system for AD detection with ASR engines. Moreover, the critical linguistic features might provide insight into further studies on the mechanism of AD.

Alzheimer's early detection in post-acute COVID-19 syndrome: a systematic review and expert consensus on preclinical assessments

Introduction

The risk of developing Alzheimer's disease (AD) in older adults increasingly is being discussed in the literature on Post-Acute COVID-19 Syndrome (PACS). Remote digital Assessments for Preclinical AD (RAPAs) are becoming more important in screening for early AD, and should always be available for PACS patients, especially for patients at risk of AD. This systematic review examines the potential for using RAPA to identify impairments in PACS patients, scrutinizes the supporting evidence, and describes the recommendations of experts regarding their use.

Methods

We conducted a thorough search using the PubMed and Embase databases. Systematic reviews (with or without meta-analysis), narrative reviews, and observational studies that assessed patients with PACS on specific RAPAs were included. The RAPAs that were identified looked for impairments in olfactory, eye-tracking, graphical, speech and language, central auditory, or spatial navigation abilities. The recommendations' final grades were determined by evaluating the strength of the evidence and by having a consensus discussion about the results of the Delphi rounds among an international Delphi consensus panel called IMPACT, sponsored by the French National Research Agency. The consensus panel included 11 international experts from France, Switzerland, and Canada.

Results

Based on the available evidence, olfaction is the most long-lasting impairment found in PACS patients. However, while olfaction is the most prevalent impairment, expert consensus statements recommend that AD olfactory screening should not be used on patients with a history of PACS at this point in time. Experts recommend that olfactory screenings can only be recommended once those under study have reported full recovery. This is particularly important for the deployment of the olfactory identification subdimension. The expert assessment that more long-term studies are needed after a period of full recovery, suggests that this consensus statement requires an update in a few years.

Conclusion

Based on available evidence, olfaction could be long-lasting in PACS patients. However, according to expert consensus statements, AD olfactory screening is not recommended for patients with a history of PACS until complete recovery has been confirmed in the literature, particularly for the identification sub-dimension. This consensus statement may require an update in a few years.

Automated detection of progressive speech changes in early Alzheimer's disease

Speech and language changes occur in Alzheimer's disease (AD), but few studies have characterized their longitudinal course. We analyzed open-ended speech samples from a prodromal-to-mild AD cohort to develop a novel composite score to characterize progressive speech changes. Participant speech from the Clinical Dementia Rating (CDR) interview was analyzed to compute metrics reflecting speech and language characteristics. We determined the aspects of speech and language that exhibited significant longitudinal change over 18 months. Nine acoustic and linguistic measures were combined to create a novel composite score. The speech composite exhibited significant correlations with primary and secondary clinical endpoints and a similar effect size for detecting longitudinal change. Our results demonstrate the feasibility of using automated speech processing to characterize longitudinal change in early AD. Speech-based composite scores could be used to monitor change and detect response to treatment in future research.

HIGHLIGHTS

Longitudinal speech samples were analyzed to characterize speech changes in early AD.Acoustic and linguistic measures showed significant change over 18 months.A novel speech composite score was computed to characterize longitudinal change.The speech composite correlated with primary and secondary trial endpoints.Automated speech analysis could facilitate remote, high frequency monitoring in AD.