Distinct patterns of imprecise consonant articulation among Parkinson's disease, progressive supranuclear palsy and multiple system atrophy

Sex Differences in the Speech of Persons With and Without Parkinson's Disease

BACKGROUND

Sex differences are apparent in the prevalence and the clinical presentation of Parkinson's disease (PD), but their effects on speech have been less studied.

METHOD

Speech acoustics of persons with (34 females and 34 males) and without (age- and sex-matched) PD were examined, assessing the effects of PD diagnosis and sex on ratings of dysarthria severity and acoustic measures of phonation (fundamental frequency standard deviation, smoothed cepstral peak prominence), speech rate (net syllables per second, percent pause ratio), and articulation (articulatory-acoustic vowel space, release burst precision).

RESULTS

Most measures were affected by PD (dysarthria severity, fundamental frequency standard deviation) and sex (smoothed cepstral peak prominence, net syllables per second, percent pause ratio, articulatory-acoustic vowel space), but without interactions between them. Release burst precision was differentially affected by sex in PD. Relative to those without PD, persons with PD produced fewer plosives with a single burst: females more frequently produced multiple bursts, whereas males more frequently produced no burst at all.

CONCLUSIONS

Most metrics did not indicate that speech production is differentially affected by sex in PD. Sex was, however, associated with disparate effects on release burst precision in PD, which deserves further study.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24388666.

Consonant articulation acoustics and intelligibility in Swedish speakers with Parkinson's disease: a pilot study

Imprecise consonant articulation is common in speakers with Parkinson's disease and can affect intelligibility. The research on the relationship between acoustic speech measures and intelligibility in Parkinson's disease is limited, and most of the research has been conducted on English. This pilot study investigated aspects of consonant articulation acoustics in eleven Swedish speakers with Parkinson's disease and six neurologically healthy persons. The focus of the study was on consonant cluster production, articulatory motion rate and variation, and voice onset time, and how these acoustic features correlate with speech intelligibility. Among the measures in the present study, typicality ratings of heterorganic consonant clusters /spr/ and /skr/ had the strongest correlations with intelligibility. Measures based on syllable repetition, such as repetition rate and voice onset time, showed varying results with weak to moderate correlations with intelligibility. One conclusion is that some acoustic measures may be more sensitive than others to the impact of the underlying sensory-motor impairment and dysarthria on speech production and intelligibility in speakers with Parkinson's disease. Some aspects of articulation appear to be equally demanding in terms of acoustic realisation for elderly healthy speakers and for speakers with Parkinson's disease, such as sequential motion rate measures. Clinically, this would imply that for the purpose of detecting signs of disordered speech motor control, choosing measures with less variation among older speakers without articulation impairment would lead to more robust results.

Acoustic Predictors of Ease of Understanding in Spanish Speakers With Dysarthria Associated With Parkinson's Disease

PURPOSE

The purpose of this study was to examine selected baseline acoustic features of hypokinetic dysarthria in Spanish speakers with Parkinson's disease (PD) and identify potential acoustic predictors of ease of understanding in Spanish.

METHOD

Seventeen Spanish-speaking individuals with mild-to-moderate hypokinetic dysarthria secondary to PD and eight healthy controls were recorded reading a translation of the Rainbow Passage. Acoustic measures of vowel space area, as indicated by the formant centralization ratio (FCR), envelope modulation spectra (EMS), and articulation rate were derived from the speech samples. Additionally, 15 healthy adults rated ease of understanding of the recordings on a visual analogue scale. A multiple linear regression model was implemented to investigate the predictive value of the selected acoustic parameters on ease of understanding.

RESULTS

Listeners' ease of understanding was significantly lower for speakers with dysarthria than for healthy controls. The FCR, EMS from the first 10 s of the reading passage, and the difference in EMS between the end and the beginning sections of the passage differed significantly between the two groups of speakers. Findings indicated that 67.7% of the variability in ease of understanding was explained by the predictive model, suggesting a moderately strong relationship between the acoustic and perceptual domains.

CONCLUSIONS

Measures of envelope modulation spectra were found to be highly significant model predictors of ease of understanding of Spanish-speaking individuals with hypokinetic dysarthria associated with PD. Articulation rate was also found to be important (albeit to a lesser degree) in the predictive model. The formant centralization ratio should be further examined with a larger sample size and more severe dysarthria to determine its efficacy in predicting ease of understanding.

Quantifying articulatory impairments in neurodegenerative motor diseases: A scoping review and meta-analysis of interpretable acoustic features

PURPOSE

Neurodegenerative motor diseases (NMDs) have devastating effects on the lives of patients and their loved ones, in part due to the impact of neurologic abnormalities on speech, which significantly limits functional communication. Clinical speech researchers have thus spent decades investigating speech features in populations suffering from NMDs. Features of impaired articulatory function are of particular interest given their detrimental impact on intelligibility, their ability to encode a variety of distinct movement disorders, and their potential as diagnostic indicators of neurodegenerative diseases. The objectives of this scoping review were to identify (1) which components of articulation (i.e. coordination, consistency, speed, precision, and repetition rate) are the most represented in the acoustic literature on NMDs; (2) which acoustic articulatory features demonstrate the most potential for detecting speech motor dysfunction in NMDs; and (3) which articulatory components are the most impaired within each NMD.

METHOD

This review examined literature published between 1976 and 2020. Studies were identified from six electronic databases using predefined key search terms. The first research objective was addressed using a frequency count of studies investigating each articulatory component, while the second and third objectives were addressed using meta-analyses.

RESULT

Findings from 126 studies revealed a considerable emphasis on articulatory precision. Of the 24 features included in the meta-analyses, vowel dispersion/distance and stop gap duration exhibited the largest effects when comparing the NMD population to controls. The meta-analyses also revealed divergent patterns of articulatory performance across disease types, providing evidence of unique profiles of articulatory impairment.

CONCLUSION

This review illustrates the current state of the literature on acoustic articulatory features in NMDs. By highlighting the areas of need within each articulatory component and disease group, this work provides a foundation on which clinical researchers, speech scientists, neurologists, and computer science engineers can develop research questions that will both broaden and deepen the understanding of articulatory impairments in NMDs.

Oromotor Nonverbal Performance and Speech Motor Control: Theory and Review of Empirical Evidence

This position paper offers a perspective on the long-standing debate concerning the role of oromotor, nonverbal gestures in understanding typical and disordered speech motor control secondary to neurological disease. Oromotor nonverbal tasks are employed routinely in clinical and research settings, but a coherent rationale for their use is needed. The use of oromotor nonverbal performance to diagnose disease or dysarthria type, versus specific aspects of speech production deficits that contribute to loss of speech intelligibility, is argued to be an important part of the debate. Framing these issues are two models of speech motor control, the Integrative Model (IM) and Task-Dependent Model (TDM), which yield contrasting predictions of the relationship between oromotor nonverbal performance and speech motor control. Theoretical and empirical literature on task specificity in limb, hand, and eye motor control is reviewed to demonstrate its relevance to speech motor control. The IM rejects task specificity in speech motor control, whereas the TDM is defined by it. The theoretical claim of the IM proponents that the TDM requires a special, dedicated neural mechanism for speech production is rejected. Based on theoretical and empirical information, the utility of oromotor nonverbal tasks as a window into speech motor control is questionable.

CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments

Parkinson's disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson's disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.

Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease

Motor impairments are only one aspect of Parkinson's disease (PD), which also include cognitive and linguistic impairments. Speech-derived interpretable biomarkers may help clinicians diagnose PD at earlier stages and monitor the disorder's evolution over time. This study focuses on the multilingual evaluation of a composite array of biomarkers that facilitate PD evaluation from speech. Hypokinetic dysarthria, a motor speech disorder associated with PD, has been extensively analyzed in previously published studies on automatic PD evaluation, with a relative lack of inquiry into language and task variability. In this study, we explore certain acoustic, linguistic, and cognitive information encoded within the speech of several cohorts with PD. A total of 24 biomarkers were analyzed from American English, Italian, Castilian Spanish, Colombian Spanish, German, and Czech by conducting a statistical analysis to evaluate which biomarkers best differentiate people with PD from healthy participants. The study leverages conceptual robustness as a criterion in which a biomarker behaves the same, independent of the language. Hence, we propose a set of speech-based biomarkers that can effectively help evaluate PD while being language-independent. In short, the best acoustic and cognitive biomarkers permitting discrimination between experimental groups across languages were fundamental frequency standard deviation, pause time, pause percentage, silence duration, and speech rhythm standard deviation. Linguistic biomarkers representing the length of the narratives and the number of nouns and auxiliaries also provided discrimination between groups. Altogether, in addition to being significant, these biomarkers satisfied the robustness requirements.

The efficacy of acoustic-based articulatory phenotyping for characterizing and classifying four divergent neurodegenerative diseases using sequential motion rates

Despite the impacts of neurodegeneration on speech function, little is known about how to comprehensively characterize the resulting speech abnormalities using a set of objective measures. Quantitative phenotyping of speech motor impairments may have important implications for identifying clinical syndromes and their underlying etiologies, monitoring disease progression over time, and improving treatment efficacy. The goal of this research was to investigate the validity and classification accuracy of comprehensive acoustic-based articulatory phenotypes in speakers with distinct neurodegenerative diseases. Articulatory phenotypes were characterized based on acoustic features that were selected to represent five components of motor performance: Coordination, Consistency, Speed, Precision, and Rate. The phenotypes were first used to characterize the articulatory abnormalities across four progressive neurologic diseases known to have divergent speech motor deficits: amyotrophic lateral sclerosis (ALS), progressive ataxia (PA), Parkinson's disease (PD), and the nonfluent variant of primary progressive aphasia and progressive apraxia of speech (nfPPA + PAOS). We then examined the efficacy of articulatory phenotyping for disease classification. Acoustic analyses were conducted on audio recordings of 217 participants (i.e., 46 ALS, 52 PA, 60 PD, 20 nfPPA + PAOS, and 39 controls) during a sequential speech task. Results revealed evidence of distinct articulatory phenotypes for the four clinical groups and that the phenotypes demonstrated strong classification accuracy for all groups except ALS. Our results highlight the phenotypic variability present across neurodegenerative diseases, which, in turn, may inform (1) the differential diagnosis of neurological diseases and (2) the development of sensitive outcome measures for monitoring disease progression or assessing treatment efficacy.

Early detection of Parkinson’s disease from multiple signal speech: Based on Mandarin language dataset

Parkinson’s disease (PD) is a neurodegenerative disorder that negatively affects millions of people. Early detection is of vital importance. As recent researches showed dysarthria level provides good indicators to the computer-assisted diagnosis and remote monitoring of patients at the early stages. It is the goal of this study to develop an automatic detection method based on newest collected Chinese dataset. Unlike English, no agreement was reached on the main features indicating language disorders due to vocal organ dysfunction. Thus, one of our approaches is to classify the speech phonation and articulation with a machine learning-based feature selection model. Based on a relatively big sample, three feature selection algorithms (LASSO, mRMR, Relief-F) were tested to select the vocal features extracted from speech signals collected in a controlled setting, followed by four classifiers (Naïve Bayes, K-Nearest Neighbor, Logistic Regression and Stochastic Gradient Descent) to detect the disorder. The proposed approach shows an accuracy of 75.76%, sensitivity of 82.44%, specificity of 73.15% and precision of 76.57%, indicating the feasibility and promising future for an automatic and unobtrusive detection on Chinese PD. The comparison among the three selection algorithms reveals that LASSO selector has the best performance regardless types of vocal features. The best detection accuracy is obtained by SGD classifier, while the best resulting sensitivity is obtained by LR classifier. More interestingly, articulation features are more representative and indicative than phonation features among all the selection and classifying algorithms. The most prominent articulation features are F1, F2, DDF1, DDF2, BBE and MFCC.

Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

While speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Desempeño fonético-acústico de vocales en hablantes del español chileno con enfermedad de Parkinson en estadios iniciales

La articulación de los sonidos lingüísticos consonánticos y vocálicos se afecta en la enfermedad de Parkinson (EP). En el caso de las vocales, esta alteración se manifiesta acústicamente en la estructura formántica y en el área de espacio vocálico. Debido a que no se ha explorado esta temática en Chile, la investigación tuvo por objetivo contrastar el desempeño fonético-acústico de vocales entre hablantes del español chileno con EP inicial y sin la enfermedad. Se efectuó un estudio cuantitativo, cuasiexperimental y correlacional. 15 hablantes con EP (M=69.6 años, DE=7.46) y 15 sin EP (M=70.07 años, DE=7.75) leyeron 30 frases que contenían las cinco vocales del español de Chile. Se analizaron los centros de frecuencia (F1 y F2) y los anchos de banda (B1 y B2) de los formantes vocálicos, y cinco índices del área de espacio vocálico. Se evidenciaron diferencias en el B2 de /i/ y /u/ entre personas con y sin EP; en el F1 de /e/ y /u/, el F2 de /u/, el B1 de /e/ y el B2 de /o/ entre hombres con y sin EP; y en el B2 de /i/ entre mujeres con y sin EP (p<.05). De esta forma, se reporta el desempeño acústico de las vocales en hablantes del español chileno con enfermedad de Parkinson.

Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study

BACKGROUND

An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria.

METHOD

The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve.

RESULTS

The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant.

CONCLUSION

Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

Random Forest Algorithm Based on Speech for Early Identification of Parkinson's Disease

To investigate the effectiveness of identifying patients with Parkinson's disease (PD) from speech signals, various acoustic parameters including prosodic and segmental features are extracted from speech and then the random forest classification (RF) algorithm based on these acoustic parameters is applied to diagnose early-stage PD patients. To validate the proposed method of RF algorithm in early-stage PD identification, this study compares the accuracy rate of RF with that of neurologists' judgments based on auditory test outcomes, and the results clearly show the superiority of the proposed method over its rival. Random forest algorithm based on speech can improve the accuracy of patients' identification, which provides an efficient auxiliary method in the early diagnosis of PD patients.

Vocal changes in a zebra finch model of Parkinson's disease characterized by alpha-synuclein overexpression in the song-dedicated anterior forebrain pathway

Deterioration in the quality of a person's voice and speech is an early marker of Parkinson's disease (PD). In humans, the neural circuit that supports vocal motor control consists of a cortico-basal ganglia-thalamo-cortico loop. The basal ganglia regions, striatum and globus pallidus, in this loop play a role in modulating the acoustic features of vocal behavior such as loudness, pitch, and articulatory rate. In PD, this area is implicated in pathogenesis. In animal models of PD, the accumulation of toxic aggregates containing the neuronal protein alpha-synuclein (αsyn) in the midbrain and striatum result in limb and vocal motor impairments. It has been challenging to study vocal impairments given the lack of well-defined cortico-basal ganglia circuitry for vocalization in rodent models. Furthermore, whether deterioration of voice quality early in PD is a direct result of αsyn-induced neuropathology is not yet known. Here, we take advantage of the well-characterized vocal circuits of the adult male zebra finch songbird to experimentally target a song-dedicated pathway, the anterior forebrain pathway, using an adeno-associated virus expressing the human wild-type αsyn gene, SNCA. We found that overexpression of αsyn in this pathway coincides with higher levels of insoluble, monomeric αsyn compared to control finches. Impairments in song production were also detected along with shorter and poorer quality syllables, which are the most basic unit of song. These vocal changes are similar to the vocal abnormalities observed in individuals with PD.

Distinct patterns of speech disorder in early-onset and late-onset de-novo Parkinson's disease

Substantial variability and severity of dysarthric patterns across Parkinson’s disease (PD) patients may reflect distinct phenotypic differences. We aimed to compare patterns of speech disorder in early-onset PD (EOPD) and late-onset PD (LOPD) in drug-naive patients at early stages of disease. Speech samples were acquired from a total of 96 participants, including two subgroups of 24 de-novo PD patients and two subgroups of 24 age- and sex-matched young and old healthy controls. The EOPD group included patients with age at onset below 51 (mean 42.6, standard deviation 6.1) years and LOPD group patients with age at onset above 69 (mean 73.9, standard deviation 3.0) years. Quantitative acoustic vocal assessment of 10 unique speech dimensions related to respiration, phonation, articulation, prosody, and speech timing was performed. Despite similar perceptual dysarthria severity in both PD subgroups, EOPD showed weaker inspirations (p = 0.03), while LOPD was characterized by decreased voice quality (p = 0.02) and imprecise consonant articulation (p = 0.03). In addition, age-independent occurrence of monopitch (p < 0.001), monoloudness (p = 0.008), and articulatory decay (p = 0.04) was observed in both PD subgroups. The worsening of consonant articulation was correlated with the severity of axial gait symptoms (r = 0.38, p = 0.008). Speech abnormalities in EOPD and LOPD share common features but also show phenotype-specific characteristics, likely reflecting the influence of aging on the process of neurodegeneration. The distinct pattern of imprecise consonant articulation can be interpreted as an axial motor symptom of PD.

Parkinson's Disease Detection Based on Running Speech Data From Phone Calls

OBJECTIVE

Parkinson's Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living offers the potential for transforming the disease assessment and accelerating PD diagnosis.

METHODS

A privacy-aware method for classifying PD patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed here. Voice features from running speech signals were extracted from recordings passively captured over voice phone calls. Features are fed in a language-aware training of multiple- and single-instance learning classifiers, along with demographic variables, exploiting a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients) to classify PD.

RESULTS

By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of-sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. Comparative analysis with other approaches for language-aware PD detection justified the efficiency of the proposed one, considering the ecological validity of the acquired voice data.

CONCLUSIONS

The present work demonstrates increased robustness in PD detection using voice data captured in-the-wild.

SIGNIFICANCE

A high-frequency, privacy-aware and unobtrusive PD screening tool is introduced for the first time, based on analysis of voice samples captured during routine phone calls.

Automatic boost articulation therapy in adults with dysarthria: Acceptability, usability and user interaction

BACKGROUND

Imprecise articulation has a negative impact on speech intelligibility. Therefore, treatment of articulation is clinically relevant in patients with dysarthria. In order to be effective and according to the principles of motor learning, articulation therapy needs to be intensive, well organized, with adequate feedback and requires frequent practice.

AIMS

The aims of this pilot study are (1) to evaluate the feasibility of a virtual articulation therapy (VAT) to guide patients with dysarthria through a boost articulation therapy (BArT) program; (2) to evaluate the acoustic models' performance used for automatic phonological error detection; and (3) to validate the system by end-users from their perspective.

METHODS & PROCEDURES

The VAT provides an extensive and well-structured package of exercises with visual and auditory modelling and adequate feedback on the utterances. The tool incorporates automated methods to detect phonological errors, which are specifically designed to analyse Dutch speech production. A total of 14 subjects with dysarthria evaluated the acceptability, usability and user interaction with the VAT based on two completed therapy sessions using a self-designed questionnaire.

OUTCOMES & RESULTS

In general, participants were positive about the new computer-based therapy approach. The algorithm performance for phonological error detection shows it to be accurate, which contributes to adequate feedback of utterance production. The results of the study indicate that the VAT has a user-friendly interface that can be used independently by patients with dysarthria who have sufficient cognitive, linguistic, motoric and sensory skills to benefit from speech therapy. Recommendations were given by the end-users to further optimize the program and to ensure user engagement.

CONCLUSIONS & IMPLICATIONS

The initial implementation of an automatic BArT shows it to be feasible and well accepted by end-users. The tool is an appropriate solution to increase the frequency and intensity of articulation training that supports traditional methods.

WHAT THIS PAPER ADDS

What is already known on the subject Behavioural interventions to improve articulation in patients with dysarthria demand intensive treatments, repetitive practice and feedback. However, the current treatments are mainly limited in time to the interactive sessions in the presence of speech-language pathology. Automatic systems addressing the needs of individuals with dysarthria are scarce. This study evaluates the feasibility of a VAT program and investigates its acceptability, usability and user interaction. What this paper adds to existing knowledge The computer-based speech therapy approach developed and applied in this study intends to support intensive articulation training of patients with dysarthria. The virtual speech therapy offers the possibility of an individualized and customized therapy programme, with an extensive database of exercises, visual and auditory models of the target utterances, and providing adequate feedback based on automatic acoustic analysis of speech. What are the potential or actual clinical implications of this work? The automatic BArT overcomes the limitation in time of face-to-face traditional speech therapy. It offers patients the opportunity to have access to speech therapy more intensively and frequently in their home environment.

Early recognition and diagnosis of multiple system atrophy: best practice and emerging concepts

Introduction: Multiple system atrophy (MSA) is a progressive degenerative disorder of the central and autonomic nervous systems characterized by parkinsonism, cerebellar ataxia, dysautonomia, and pyramidal signs. The confirmatory diagnosis is pathological, but clinical-diagnostic criteria have been developed to help clinicians. To date, the early diagnosis of MSA is challenging due to the lack of reliable diagnostic biomarkers.Areas covered: The authors reappraised the main clinical, neurophysiological, imaging, genetic, and laboratory evidence to help in the early diagnosis of MSA in the clinical and in the research settings. They also addressed the practical clinical issues in the differential diagnosis between MSA and other parkinsonian and cerebellar syndromes. Finally, the authors summarized the unmet needs in the early diagnosis of MSA and proposed the next steps for future research efforts in this field.Expert opinion: In the last decade, many advances have been achieved to help the correct MSA diagnosis since early stages. In the next future, the early diagnosis and correct classification of MSA, together with a better knowledge of the causative mechanisms of the disease, will hopefully allow the identification of suitable candidates to enroll in clinical trials and select the most appropriate disease-modifying strategies to slow down disease progression.

Cognitive Determinants of Dysarthria in Parkinson's Disease: An Automated Machine Learning Approach

BACKGROUND

Dysarthric symptoms in Parkinson's disease (PD) vary greatly across cohorts. Abundant research suggests that such heterogeneity could reflect subject-level and task-related cognitive factors. However, the interplay of these variables during motor speech remains underexplored, let alone by administering validated materials to carefully matched samples with varying cognitive profiles and combining automated tools with machine learning methods.

OBJECTIVE

We aimed to identify which speech dimensions best identify patients with PD in cognitively heterogeneous, cognitively preserved, and cognitively impaired groups through tasks with low (reading) and high (retelling) processing demands.

METHODS

We used support vector machines to analyze prosodic, articulatory, and phonemic identifiability features. Patient groups were compared with healthy control subjects and against each other in both tasks, using each measure separately and in combination.

RESULTS

Relative to control subjects, patients in cognitively heterogeneous and cognitively preserved groups were best discriminated by combined dysarthric signs during reading (accuracy = 84% and 80.2%). Conversely, patients with cognitive impairment were maximally discriminated from control subjects when considering phonemic identifiability during retelling (accuracy = 86.9%). This same pattern maximally distinguished between cognitively spared and impaired patients (accuracy = 72.1%). Also, cognitive (executive) symptom severity was predicted by prosody in cognitively preserved patients and by phonemic identifiability in cognitively heterogeneous and impaired groups. No measure predicted overall motor dysfunction in any group.

CONCLUSIONS

Predominant dysarthric symptoms appear to be best captured through undemanding tasks in cognitively heterogeneous and preserved cohorts and through cognitively loaded tasks in patients with cognitive impairment. Further applications of this framework could enhance dysarthria assessments in PD. © 2021 International Parkinson and Movement Disorder Society.

Single Word Intelligibility of Individuals with Parkinson's Disease in Noise: Pre-Specified Secondary Outcome Variables from a Randomized Control Trial (RCT) Comparing Two Intensive Speech Treatments (LSVT LOUD vs. LSVT ARTIC)

The majority of people with Parkinson's disease (PD) experience both prosodic changes (reduced vocal volume, reduced pitch range) and articulatory changes (imprecise articulation) that often limit speech intelligibility and may contribute to significant declines in quality of life. We conducted a randomized control trial comparing two intensive treatments, voice (LSVT LOUD) or articulation (LSVT ARTIC) to assess single word intelligibility in the presence of background noise (babble and mall). Participants (64 PD and 20 Healthy) read words from the diagnostic rhyme test (DRT), an ANSI Standard for measuring intelligibility of speech, before and after one month (treatment or no treatment). Teams of trained listeners blindly rated the data. Speech intelligibility of words in the presence of both noise conditions improved in PD participants who had LSVT LOUD compared to the groups that had LSVT ARTIC or no treatment. Intensive speech treatment targeting prominent prosodic variables in LSVT LOUD had a positive effect on speech intelligibility at the single word level in PD.

Motor Speech Disorders and Communication Limitations in Progressive Supranuclear Palsy

Purpose This study describes motor speech disorders and associated communication limitations in six variants of progressive supranuclear palsy (PSP). Method The presence, nature, and severity of dysarthria and apraxia of speech (AOS) were documented, along with scores on the Apraxia of Speech Rating Scale-Version 3 (ASRS-3) for 77 (40 male and 37 female) patients with PSP. Clinician-estimated and patient-estimated communication limitations were rated using the Motor Speech Disorders Severity Rating (MSDSR) Scale and the Communicative Effectiveness Survey (CES), respectively. Descriptive statistics were calculated for each of these dependent variables. One-tailed t tests were conducted to test mean differences in ASRS-3 and CES between participants with and without AOS and between participants with and without dysarthria. Spearman rank correlations were calculated between ASRS-3 scores and clinical judgments of AOS and dysarthria severity and between MSDSR and CES ratings. Results Nine participants (12%) had normal speech. Eighty-seven percent exhibited dysarthria; hypokinetic and mixed hypokinetic-spastic dysarthria were observed most frequently. AOS was observed in 19.5% of participants across all variants, but in only 10% exclusive of the PSP speech and language variant. Nearly half presented with AOS in which neither phonetic nor prosodic features clearly predominated. The mean ASRS-3 score for participants with AOS was significantly higher than for those without and correlated strongly with clinician judgment of AOS severity. Mean ASRS-3 was higher for participants with dysarthria than for those without but correlated weakly with dysarthria severity. Mean MSDSR and CES ratings were lower in participants with AOS compared to those without and moderately correlated with each other. Conclusions Motor speech disorders that negatively impact communicative effectiveness are common in PSP and occur in many variants. This is the first description of motor speech disorders across PSP variants, setting the stage for future research characterizing neuroanatomical correlates, progression of motor speech disorders, and benefits of targeted interventions. Supplemental Material https://doi.org/10.23641/asha.14111837.

Speech Biomarkers in Rapid Eye Movement Sleep Behavior Disorder and Parkinson Disease

Objective

This multilanguage study used simple speech recording and high‐end pattern analysis to provide sensitive and reliable noninvasive biomarkers of prodromal versus manifest α‐synucleinopathy in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and early‐stage Parkinson disease (PD).

Methods

We performed a multicenter study across the Czech, English, German, French, and Italian languages at 7 centers in Europe and North America. A total of 448 participants (337 males), including 150 with iRBD (mean duration of iRBD across language groups 0.5–3.4 years), 149 with PD (mean duration of disease across language groups 1.7–2.5 years), and 149 healthy controls were recorded; 350 of the participants completed the 12‐month follow‐up. We developed a fully automated acoustic quantitative assessment approach for the 7 distinctive patterns of hypokinetic dysarthria.

Results

No differences in language that impacted clinical parkinsonian phenotypes were found. Compared with the controls, we found significant abnormalities of an overall acoustic speech severity measure via composite dysarthria index for both iRBD (p = 0.002) and PD (p < 0.001). However, only PD (p < 0.001) was perceptually distinct in a blinded subjective analysis. We found significant group differences between PD and controls for monopitch (p < 0.001), prolonged pauses (p < 0.001), and imprecise consonants (p = 0.03); only monopitch was able to differentiate iRBD patients from controls (p = 0.004). At the 12‐month follow‐up, a slight progression of overall acoustic speech impairment was noted for the iRBD (p = 0.04) and PD (p = 0.03) groups.

Interpretation

Automated speech analysis might provide a useful additional biomarker of parkinsonism for the assessment of disease progression and therapeutic interventions. ANN NEUROL 2021;90:62–75

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Guidelines for Speech Recording and Acoustic Analyses in Dysarthrias of Movement Disorders

Most patients with movement disorders have speech impairments resulting from sensorimotor abnormalities that affect phonatory, articulatory, and prosodic speech subsystems. There is widespread cross-discipline use of speech recordings for diagnostic and research purposes, despite which there are no specific guidelines for a standardized method. This review aims to combine the specific clinical presentations of patients with movement disorders, existing acoustic assessment protocols, and technological advances in capturing speech to provide a basis for future research in this field and to improve the consistency of clinical assessments. We considered 3 areas: the recording environment (room, seating, background noise), the recording process (instrumentation, vocal tasks, elicitation of speech samples), and the acoustic outcome data. Four vocal tasks, namely, sustained vowel, sequential and alternating motion rates, reading passage, and monologues, are integral aspects of motor speech assessment. Fourteen acoustic vocal speech features, including their hypothesized pathomechanisms with regard to typical occurrences in hypokinetic or hyperkinetic dysarthria, are hereby recommended for quantitative exploratory analysis. Using these acoustic features and experimental speech data, we demonstrated that the hyperkinetic dysarthria group had more affected speech dimensions compared with the healthy controls than had the hypokinetic speakers. Several contrasting speech patterns between both dysarthrias were also found. This article is the first attempt to provide initial recommendations for a standardized way of recording the voice and speech of patients with hypokinetic or hyperkinetic dysarthria; thus allowing clinicians and researchers to reliably collect, acoustically analyze, and compare vocal data across different centers and patient cohorts. © 2020 International Parkinson and Movement Disorder Society.

Non-pharmacological Treatment Challenges in Early Parkinson's Disease for Axial and Cognitive Symptoms: A Mini Review

Background: Parkinson's disease (PD) is now known to be a multisystemic heterogeneous neurodegenerative disease, including a wide spectrum of both motor and non-motor symptoms. PD patients' management must encompass a multidisciplinary approach to effectively address its complex nature. There are still challenges in terms of treating axial (gait, balance, posture, speech, and swallowing) and cognitive symptoms that typically arise with disease progression becoming poorly responsive to dopaminergic or surgical treatments.

Objective: The objectives of the study are to further establish the presentation of axial and cognitive symptoms in early PD [Hoehn and Yahr (H&Y) scale ≤ 2] and to discuss the evidence for non-pharmacological approaches in early PD.

Results: Mild and subtle changes in the investigated domains can be present even in early PD. Over the last 15 years, a few randomized clinical trials have been focused on these areas. Due to the low number of studies and the heterogeneity of the results, no definitive recommendations are possible. However, positive results have been obtained, with effective treatments being high-intensity treadmill and cueing for gait disturbances, high-intensity voice treatment, video-assisted swallowing therapy for dysphagia, and warm-up exercises and Wii Fit^TM training for cognition.

Conclusions: Considering the association of motor, speech, and cognitive function, future trials should focus on multidisciplinary approaches to combined non-pharmacological management. We highlight the need for a more unified approach in managing these “orphan” symptoms, from the very beginning of the disease. The concept “the sooner the better” should be applied to multidisciplinary non-pharmacological management in PD.

Parkinson's disease or atypical parkinsonism? The importance of acoustic voice analysis in differential diagnosis of speech disorders

INTRODUCTION

Speech disorder is a common clinical manifestation in patients with Parkinson's disease and atypical parkinsonian syndromes and tends to occur before the onset of the axial parkinsonian symptoms. Due to parkinsonian features that overlap those of Parkinson's disease, the differentiation of voice and a speech disorder is a challenge for clinicians primarily in the early stage of the disease.

METHODS

Speech samples were obtained from 116 subjects including 30 cases of Parkinson's disease, 30 cases of progressive supranuclear palsy, 30 cases of multiple system atrophy, and control group consisted of 26 subjects. Differential diagnosis of dysarthria subtypes was based on the quantitative, acoustic analysis of particular speech components. Additionally, Voice Handicap Index questionnaire was taken into account to differentiate the severity of voice impairment in the study groups.

RESULTS

Our results showed significant differences in the distribution of acoustic parameters between Parkinson's disease and atypical parkinsonian syndromes. A mixed type of dysarthria with a combination of hypokinetic, spastic, and atactic features has been found in patients with atypical parkinsonism. In patients with the clinical diagnosis of the parkinsonian variant of multiple system atrophy, ataxic components of dysarthria were observed. Patients with PD presented pure hypokinetic dysarthria. Some parameters may be used as a marker for the diagnosis of the initial stage of PD. Voice impartment was significantly more frequent and severe in atypical parkinsonism than in Parkinson's disease.

CONCLUSION

Acoustic voice analysis is a very sensitive and noninvasive tool, provides objective information for the assessment of different speech components, has the specific potential to provide quantitative data essential for the improvement of the diagnostic process, and maybe a useful instrument in the differential diagnosis of parkinsonian syndromes.

Dysarthria enhancement mechanism under external clear speech instruction in Parkinson's disease, progressive supranuclear palsy and multiple system atrophy

Clear speech refers to intentionally modifying conversational speech to maximise intelligibility. This study aimed to compare the speech behaviour of patients with progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and Parkinson's disease (PD) under conversational and clear speech conditions to gain greater pathophysiological insight. A total of 68 participants including 17 PD, 17 MSA, 17 PSP and 17 healthy controls (HC) performed two readings of the same standardized passage. During the first reading, participants were instructed to read the text in an ordinary way, while during the second reading to read the text as clearly as possible. Acoustic analyses were based upon measurements of mean loudness, loudness variability, pitch variability, vowel articulation, articulation rate and speech severity. During clear speech production, PD patients were able to achieve improvements mainly in loudness (p < 0.05) and pitch variability (p < 0.001), leading to a reduction in overall speech severity (p < 0.001), whereas PSP and MSA patients were able to modulate only articulation rate (p < 0.05). Contrary to HC and PD groups, which slowed or maintained articulation rate, PSP and MSA groups employed a markedly faster articulation rate under the clear speech condition indicating an opposing approach to speech adaptation. Patients with atypical Parkinsonism showed a different strategy to intentionally improve their speech performance following a simple request to produce speech more clearly compared to PD, suggesting important therapeutic implications for speech rehabilitation management.

Prominence marking in parkinsonian speech and its correlation with motor performance and cognitive abilities

OBJECTIVES

Research suggests that people with Parkinson's disease (PwPD) do not only suffer from motor but also non-motor impairment. This interdisciplinary study investigated how prominence marking is influenced by problems on the motoric and cognitive level.

MATERIALS AND METHODS

We collected speech production data from 38 native German speakers: 19 PwPD (under medication) with a mild to moderate motor impairment, 13 males and 6 females (mean 66.2 years old, SD = 7.7), and 19 healthy age- and gender-matched control participants (mean 65.4 years old, SD = 9.3). Target words were produced in an accented and unaccented condition within a speech production task. The data were analyzed for intensity, syllable duration, F0 and vowel production. Furthermore, we assessed motor impairment and cognitive functions, i.e. working memory, task-switching, attention control and speed of information processing.

RESULTS

Both groups were able to mark prominence by increasing pitch, syllable duration and intensity and by adjusting their vowel production. Comparisons between PwPD and control participants revealed that the vowel space was smaller in PwPD even in mildly impaired speakers. Further, task-switching as an executive function, which was tested with the trail making test, was correlated with modulation of F0 and intensity in PwPD: the worse the task-switching performance, the stronger intensity and F0 were modulated (target overshoot). Moreover, motor impairment within the PwPD group was related to a decrease in the acoustic vowel space (target undershoot), which further resulted in a decrease in speech intelligibility and naturalness. This behaviour of target over- and undershoot indicates an inefficient way of prominence marking in PwPD with mildly affected speech.

CONCLUSION

PwPD with signs of mild dysarthria did not differ from the control speakers with respect to their strategies of prominence marking. However, only the PwPD overused F0 and intensity in prominent positions. Overmodulation of F0 and intensity was correlated with the patient's task-switching ability and reflected abnormalities in the regulatory mechanism for expressing prosodic prominence. This is the first study to report a link between cognitive skills and speech production at the phonetic level in PwPD.

Automated Assessment of Oral Diadochokinesis in Multiple Sclerosis Using a Neural Network Approach: Effect of Different Syllable Repetition Paradigms

Slow and irregular oral diadochokinesis represents an important manifestation of spastic and ataxic dysarthria in multiple sclerosis (MS). We aimed to develop a robust algorithm based on convolutional neural networks for the accurate detection of syllables from different types of alternating motion rate (AMR) and sequential motion rate (SMR) paradigms. Subsequently, we explored the sensitivity of AMR and SMR paradigms based on voiceless and voiced consonants in the detection of speech impairment. The four types of syllable repetition paradigms including /ta/, /da/, /pa/-/ta/-/ka/, and /ba/-/da/-/ga/ were collected from 120 MS patients and 60 matched healthy control speakers. Our neural network algorithm was able to correctly identify the position of individual syllables with a very high average accuracy of 97.8%, with the correct temporal detection of syllable position of 87.8% for 10 ms and 95.5% for 20 ms tolerance value. We found significantly altered diadochokinetic rate and regularity in MS compared to controls across all types of investigated tasks ( ). MS patients showed slower speech for SMR compared to AMR tasks, whereas voiced paradigms were more irregular. Objective evaluation of oral diadochokinesis using different AMR and SMR paradigms may provide important information regarding speech severity and pathophysiology of the underlying disease.

Speech disorders in Parkinson's disease: pathophysiology, medical management and surgical approaches

The prevalence of speech disorders among individuals with Parkinson's disease (PD) has been reported to be as high as 89%. Speech impairment in PD results from a combination of motor and nonmotor deficits. The production of speech depends upon the coordination of various motor activities: respiration, phonation, articulation, resonance and prosody. A speech disorder is defined as impairment in any of its inter-related components. Despite the high prevalence of speech disorders in PD, only 3–4% receive speech treatment. Treatment modalities include pharmacological intervention, speech therapy, surgery, deep brain stimulation and vocal fold augmentation. Although management of Parkinsonian dysarthria is clinically challenging, speech treatment in PD should be part of a multidisciplinary approach to patient care in this disease.

Parálisis supranuclear progresiva: estudio longitudinal a partir del análisis acústico del habla

El objetivo de este estudio es medir de forma objetiva y cuantitativa los distintos parámetros del habla en un caso de Parálisis Supranuclear Progresiva durante un periodo de cuatro años. Se realizó un análisis acústico de la vocal sostenida /a/, del automatismo “1,2,3,4” y de la repetición rápida de las sílabas /pa/, /ta/, /ka/ y /pataka/. Se consideraron diversos parámetros: la frecuencia fundamental (F0), el tiempo máximo fonatorio (TMF), la intensidad, los formantes, el shimmer, el jitter, el ruido en relación con los armónicos (NHR), la duración y el número de sílabas, el “Voice Onset Time” (VOT), la tasa de habla, la tasa de silencios y los aspectos prosódicos del habla. Las alteraciones en la voz y en el habla evolucionan hacia la imprecisión articulatoria, una pobre calidad vocal, reducido TMF, reducida tasa del habla, disminución de la variabilidad de la entonación y pausas prolongadas en las diadococinesias. El análisis reveló la coincidencia en el tiempo entre el deterioro progresivo del habla en general y el elevado porcentaje de cierres incompletos en el VOT, con la disfagia

Rhythmic performance in hypokinetic dysarthria: Relationship between reading, spontaneous speech and diadochokinetic tasks

•We investigated speech rhythm in people with Parkinson’s Disease (PwPD) and controls. •Even mildly affected PwPD differed from controls in their rhythmic performance. •PwPD showed less difference between reading and spontaneous speech. •Spontaneous speech highlighted more differences between speakers than reading. •DDK performance did not relate to rhythmic behaviour in connected speech.

Utility and accuracy of perceptual voice and speech distinctions in the diagnosis of Parkinson’s disease, PSP and MSA-P

Aim: To determine if perceptual speech measures distinguish people with Parkinson’s disease (PD), multiple system atrophy with predominant parkinsonism (MSA-P) and progressive supranuclear palsy (PSP). Methods: Speech–language therapists blind to patient characteristics employed clinical rating scales to evaluate speech/voice in 24 people with clinically diagnosed PD, 17 with PSP and 9 with MSA-P, matched for disease duration (mean 4.9 years, standard deviation 2.2). Results: No consistent intergroup differences appeared on specific speech/voice variables. People with PD were significantly less impaired on overall speech/voice severity. Analyses by severity suggested further investigation around laryngeal, resonance and fluency changes may characterize individual groups. Conclusion: MSA-P and PSP compared with PD were distinguished by severity of speech/voice deterioration, but individual speech/voice parameters failed to consistently differentiate groups.