Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson's disease

Neuroanatomical frameworks for volitional control of breathing and orofacial behaviors

Breathing is the only vital function that can be volitionally controlled. However, a detailed understanding how volitional (cortical) motor commands can transform vital breathing activity into adaptive breathing patterns that accommodate orofacial behaviors such as swallowing, vocalization or sniffing remains to be developed. Recent neuroanatomical tract tracing studies have identified patterns and origins of descending forebrain projections that target brain nuclei involved in laryngeal adductor function which is critically involved in orofacial behavior. These nuclei include the midbrain periaqueductal gray and nuclei of the respiratory rhythm and pattern generating network in the brainstem, specifically including the pontine Kölliker-Fuse nucleus and the pre-Bötzinger complex in the medulla oblongata. This review discusses the functional implications of the forebrain-brainstem anatomical connectivity that could underlie the volitional control and coordination of orofacial behaviors with breathing.

Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Speech impairments often emerge as one of the primary indicators of Parkinson's disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

Functional data analysis of prosodic prominence in Parkinson's disease: a pilot study

This study aims to reveal dynamic changes in prosodic prominence patterns associated with Parkinson's disease (PD). To fulfill this purpose, the study proposes an exploratory methodology involving measuring a novel syllable-based prosody index (SPI) and performing functional principal component analyses (fPCAs) in a semi-automatic manner. First, SPI trajectories were collected from 31 speakers with PD before and after speech therapy and from 36 healthy controls. Then, the SPI trajectories were converted to continuous functions using B-splines. Finally, the functional SPIs were examined using fPCAs. The results showed that PD was associated with an increase of overall prominence for male speakers. The findings regarding higher prominence patterns in PD were supported by traditional phonetic measurements. For female speakers, however, there were no significant differences in prosodic prominence between speakers with PD and healthy controls. The results encourage to explore the proposed methodology also in analyses of other forms of atypical speech.

Effects of speech rate modifications on phonatory acoustic outcomes in Parkinson's disease

Speech rate reduction is a global speech therapy approach for speech deficits in Parkinson's disease (PD) that has the potential to result in changes across multiple speech subsystems. While the overall goal of rate reduction is usually improvements in speech intelligibility, not all people with PD benefit from this approach. Speech rate is often targeted as a means of improving articulatory precision, though less is known about rate-induced changes in other speech subsystems that could help or hinder communication. The purpose of this study was to quantify phonatory changes associated with speech rate modification across a broad range of speech rates from very slow to very fast in talkers with and without PD. Four speaker groups participated: younger and older healthy controls, and people with PD with and without deep brain stimulation of the subthalamic nucleus (STN-DBS). Talkers read aloud standardized sentences at 7 speech rates elicited using magnitude production: habitual, three slower rates, and three faster rates. Acoustic measures of speech intensity, cepstral peak prominence, and fundamental frequency were measured as a function of speech rate and group. Overall, slower rates of speech were associated with differential effects on phonation across the four groups. While all talkers spoke at a lower pitch in slow speech, younger talkers showed increases in speech intensity and cepstral peak prominence, while talkers with PD and STN-DBS showed the reverse pattern. Talkers with PD without STN-DBS and older healthy controls behaved in between these two extremes. At faster rates, all groups uniformly demonstrated increases in cepstral peak prominence. While speech rate reductions are intended to promote positive changes in articulation to compensate for speech deficits in dysarthria, the present results highlight that undesirable changes may be invoked across other subsystems, such as at the laryngeal level. In particular, talkers with STN-DBS, who often demonstrate speech deterioration following DBS surgery, demonstrated more phonatory detriments at slowed speech rates. Findings have implications for speech rate candidacy considerations and speech motor control processes in PD.

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.

Bridging the Gap: Association between Objective and Subjective Outcomes of Communication Performance in People with Parkinson's Disease Evaluated for Deep Brain Stimulation

Background

Decrements in verbal fluency following deep brain stimulation (DBS) in people with Parkinson's disease (PwP) are common. As such, verbal fluency tasks are used in assessing DBS candidacy and target selection. However, the correspondence between testing performance and the patient's perception of communication abilities is not well-established.

Methods

The Communication Participation Item Bank (CPIB) was administered to 85 PwP during pre-DBS neuropsychological evaluations. Central tendencies for CPIB responses and correlations between CPIB total scores, clinical and demographic factors, and language-based tasks were examined.

Results

Most PwP indicated some degree of communication interference on the CPIB. Worse scores on semantic fluency and greater motor impairment were associated with more communication interference.

Conclusions

Our findings suggest an incomplete correspondence between commonly used language-based tests and patient-reported outcomes of communication abilities. The need for a functional communication instrument that reflects the different aspects of communication abilities in functional contexts is emphasized.

Cognitive profile of patients with and without speech impairment in Parkinson's disease

Cognitive functions have been the subject of studies evaluating the pathophysiological mechanism of speech control.

Objective

To compare the groups of patients with and without speech disorders with cognitive assessment, demographic, and clinical data (disease duration, functionality, and motor symptoms).

Methods

Retrospective, cross-sectional study. Patients were evaluated using the Addenbrooke's Cognitive Examination III and neuropsychological tests. The following speech subsystems were analyzed: articulation, phonation, resonance, and prosody, through auditory-perceptual evaluation (based on the Protocol for the Evaluation of Acquired Speech Disorders in Individuals with Parkinson's Disease - PADAF Protocol tests), observing aspects of speech programming and execution. The patients were distributed into three subgroups (normal cognition, mild cognitive impairment, and dementia). After speech evaluation, they were divided into two subgroups (with and without speech disorders).

Results

A total of 150 patients participated in this study, 104 men and 46 women, 63.58 (8.81) years of age, 11.03 (4.00) years of schooling, 6.61 (4.69) years of disease progression, and with the highest proportion of individuals in stage I-II of the Hoehn & Yarh (H&Y) scale (86, or 57.33%). Statistically significant differences were observed between subgroups with and without speech alteration. Worse performance was verified in the Trail Making Test (TMT) TMT-Δ and a tendency of difference in the TMT-B of the subgroup with speech disorders, in addition to worse severity of motor symptoms (H&Y) and cognitive complaints.

Conclusion

Individuals with speech disorders brought more frequent cognitive complaints and impairment below expected in tests assessing executive functions. Future studies, with stratification by type of speech disorder, are necessary to contribute to and validate these results.

Time-frequency analysis of speech signal using Chirplet transform for automatic diagnosis of Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world after Alzheimer's disease. Early diagnosing PD is challenging as it evolved slowly, and its symptoms eventuate gradually. Recent studies have demonstrated that changes in speech may be utilized as an excellent biomarker for the early diagnosis of PD. In this study, we have proposed a Chirplet transform (CT) based novel approach for diagnosing PD using speech signals. We employed CT to get the time-frequency matrix (TFM) of each speech recording, and we extracted time-frequency based entropy (TFE) features from the TFM. The statistical analysis demonstrates that the TFE features reflect the changes in speech that occurs in the speech due to PD, hence can be used for classifying the PD and healthy control (HC) individuals. The effectiveness of the proposed framework is validated using the vowels and words from the PC-GITA database. The genetic algorithm is utilized to select the optimum features subset, while a support vector machine (SVM), decision tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) classifiers are employed for classification. The TFE features outperform the breathiness and Mel frequency cepstral coefficients (MFCC) features. The SVM classifier is most effective compared to other machine-learning classifiers. The highest classification accuracy rates of 98% and 99% are achieved using the vowel /a/ and word /atleta/, respectively. The results reveal that the proposed CT-based entropy features effectively diagnose PD using the speech of a person.

Raising Awareness About Language- and Culture-Specific Considerations in the Management of Dysarthria Associated With Parkinson's Disease Within the United States

PURPOSE

The purpose of this article is to raise awareness about the importance of diverting from English-centric approaches in the management of dysarthria associated with Parkinson's disease (PD) in the United States, and embracing a language- and culture-specific perspective when working with linguistically and culturally diverse populations within the context of culturally responsive, precision medicine.

METHOD

This tutorial is divided into two primary components: a critical review of language universal and language-specific characteristics of dysarthria associated with PD and their relationship with speech intelligibility, and a practical guide to culturally responsive evidence-based practice for speech-language pathologists.

CONCLUSIONS

We offer a framework for linguistically and culturally appropriate considerations when working with clients with dysarthria associated with PD. While "universal" representations of dysarthria may be part of the big picture, language-specific contributions to speakers' intelligibility should be carefully examined to maximize treatment outcomes. Additionally, an evidence-based model that fully embraces clients' wishes and values within the context of culturally responsive, precision medicine should be prioritized, a practice that may include the use of interpreters.

Intergenerational choral singing to improve communication outcomes in Parkinson's disease: Development of a theoretical framework and an integrated measurement tool

Purpose: This study presented an initial step towards developing the evidence base for intergenerational choral singing as a communication-focussed rehabilitative approach for Parkinson's disease (PD).Method: A theoretical framework was established to conceptualise the rehabilitative effect of intergenerational choral singing on four domains of communication impairments - motor drive, timing mechanism, sensorimotor integration, higher-level cognitive and affective functions - as well as activity/participation, and quality of life. A computer-assisted multidimensional acoustic analysis was developed to objectively assess the targeted domains of communication impairments. Voice Handicap Index and the World Health Organization's Quality of Life assessment-abbreviated version were used to obtain patient-reported outcomes at the activity/participation and quality of life levels. As a proof of concept, a single subject with PD was recruited to participate in 9 weekly 1-h intergenerational choir rehearsals. The subject was assessed before, 1 week post, and 8 weeks post-choir.Result: Notable trends of improvement were observed in multiple domains of communication impairments at 1 week post-choir. Some improvements were maintained at 8 weeks post-choir. Patient-reported outcomes exhibited limited pre-post changes.Conclusion: This study provided the theoretical groundwork and an empirical measurement tool for future validation of intergenerational choral singing as a novel rehabilitation for PD.

Quantifying Impairments in Swallowing Safety and Efficiency in Progressive Supranuclear Palsy and Parkinson's Disease

Dysphagia is a largely inevitable symptom in both progressive supranuclear palsy (PSP) and Parkinson's disease (PD). To date, comparative studies in these diseases have failed to detect differences in the severity of impairments in swallowing safety or efficiency, potentially due to small sample sizes and outcome measures with low sensitivity. Therefore, this study sought to address these limitations by using novel measurement methodology to comprehensively compare swallowing safety and efficiency impairments between these populations in order to better understand whether differences may exist and guide clinical management. Twenty-four participants with PSP and 24 with PD were matched for disease duration and completed flexible endoscopic evaluations of swallowing. A visual analog scale and penetration-aspiration scale quantified swallowing safety and efficiency. Bayesian multilevel models compared the frequency, severity, and variability of swallowing impairments. Individuals with PSP demonstrated greater impairments in swallowing safety, including deeper and more variable airway invasion and more frequent vocal fold and subglottic residue. Swallowing efficiency was also more impaired among individuals with PSP, including more frequent hypopharyngeal residue (with solids) and more severe residue in the oropharynx (with thin liquids and solids) and hypopharynx (with thin liquids). When airway or pharyngeal residue was present, similar within-subject variability of the amount of residue was appreciated across anatomic landmarks. This is the first study comparing the frequency, severity, and variability of swallowing impairments between PSP and PD populations. Our findings demonstrate more pronounced impairments in swallowing safety and efficiency for PSP compared to PD. These findings provide a clinically relevant characterization of swallowing measures using novel methodological and statistical approaches attempting to resolve some limitations of prior studies.

The Dysarthric Expressed Emotional Database (DEED): An audio-visual database in British English

The Dysarthric Expressed Emotional Database (DEED) is a novel, parallel multimodal (audio-visual) database of dysarthric and typical emotional speech in British English which is a first of its kind. It is an induced (elicited) emotional database that includes speech recorded in the six basic emotions: "happiness", "sadness", "anger", "surprise", "fear", and "disgust". A "neutral" state has also been recorded as a baseline condition. The dysarthric speech part includes recordings from 4 speakers: one female speaker with dysarthria due to cerebral palsy and 3 speakers with dysarthria due to Parkinson's disease (2 female and 1 male). The typical speech part includes recordings from 21 typical speakers (9 female and 12 male). This paper describes the collection of the database, covering its design, development, technical information related to the data capture, and description of the data files and presents the validation methodology. The database was validated subjectively (human performance) and objectively (automatic recognition). The achieved results demonstrated that this database will be a valuable resource for understanding emotion communication by people with dysarthria and useful in the research field of dysarthric emotion classification. The database is freely available for research purposes under a Creative Commons licence at: https://sites.google.com/sheffield.ac.uk/deed.

Monitoring the Effect of Levodopa Using Sustained Phonemes in Parkinson's Disease Patients

Parkinson's disease (PD) is a neurological disease identified by multiple symptoms, and levodopa is one of the most effective medications for treating the disease. To determine the dosage of levodopa, it is necessary to meet on a regular basis and observe motor function. The early detection and progression of the disease have been proposed using hypokinetic dysarthria. However, previous studies have not examined the effects of levodopa on speech rigorously and have provided inconsistent results. In this study, three sustained phonemes of PD patients were investigated for the effect of medication. A set of features characterizing vocal fold dynamics as well as the vocal tract coordinators were extracted from the sustained phonemes /of 28 PD patients during levodopa medication off and on states. All the features were statistically investigated and classified using a linear discriminant analysis (LDA) classifier. LDA classifier identified medication on from medication off based on the combined features from phoneme /a/, /o/ and /m/ with the accuracy=82.75% and F1-score=82.18%. Voice recording of PD patients during sustained phonemes /a/, /o/ and /m/ has the potential for identifying whether the patients are in On state or Off state of medication.Clinical Relevance- The outcomes of this study have the potential to monitor the effect and progress of levodopa on PD patients.

The Change of Vocal Tract Length in People with Parkinson's Disease

Hypokinetic dysarthria is one of the early symptoms of Parkinson's disease (PD) and has been proposed for early detection and also for monitoring of the progression of the disease. PD reduces the control of vocal tract muscles such as the tongue and lips and, therefore the length of the active vocal tract is altered. However, the change in the vocal tract length due to the disease has not been investigated. The aim of this study was to determine the difference in the apparent vocal tract length (AVTL) between people with PD and age-matched control healthy people. The phoneme, /a/ from the UCI Parkinson's Disease Classification Dataset and the Italian Parkinson's Voice and Speech Dataset were used and AVTL was calculated based on the first four formants of the sustained phoneme (F1-F4). The results show a correlation between Parkinson's disease and an increase in vocal tract length. The most sensitive feature was the AVTL calculated using the first formants of sustained phonemes (F1). The other significant finding reported in this article is that the difference is significant and only appeared in the male participants. However, the size of the database is not sufficiently large to identify the possible confounding factors such as the severity and duration of the disease, medication, age, and comorbidity factors.Clinical relevance-The outcomes of this research have the potential to improve the identification of early Parkinsonian dysarthria and monitor PD progression.

Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings

Parkinson's disease (PD) is the 2nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson's disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in telemedicine.Clinical relevance- There is an urgent need to develop non invasive biomarker of Parkinson's disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases' symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.

Articulatory speech measures can be related to the severity of multiple sclerosis

Background

Dysarthria is one of the most frequent communication disorders in patients with Multiple Sclerosis (MS), with an estimated prevalence of around 50%. However, it is unclear if there is a relationship between dysarthria and the severity or duration of the disease.

Objective

Describe the speech pattern in MS, correlate with clinical data, and compare with controls.

Methods

A group of MS patients (n = 73) matched to healthy controls (n = 37) by sex and age. Individuals with neurological and/or systemic conditions that could interfere with speech were excluded. MS group clinical data were obtained through the analysis of medical records. The speech assessment consisted of auditory-perceptual and speech acoustic analysis, from recording the following speech tasks: phonation and breathing (sustained vowel/a/); prosody (sentences with different intonation patterns) and articulation (diadochokinesis; spontaneous speech; diphthong/iu/repeatedly).

Results

In MS, 72.6% of the individuals presented mild dysarthria, with alterations in speech subsystems: phonation, breathing, resonance, and articulation. In the acoustic analysis, individuals with MS were significantly worse than the control group (CG) in the variables: standard deviation of the fundamental frequency (p = 0.001) and maximum phonation time (p = 0.041). In diadochokinesis, individuals with MS had a lower number of syllables, duration, and phonation time, but larger pauses per seconds, and in spontaneous speech, a high number of pauses were evidenced as compared to CG. Correlations were found between phonation time in spontaneous speech and the Expanded Disability Status Scale (EDSS) (r = - 0.238, p = 0.043) and phonation ratio in spontaneous speech and EDSS (r = -0.265, p = 0.023), which indicates a correlation between the number of pauses during spontaneous speech and the severity of the disease.

Conclusion

The speech profile in MS patients was mild dysarthria, with a decline in the phonatory, respiratory, resonant, and articulatory subsystems of speech, respectively, in order of prevalence. The increased number of pauses during speech and lower rates of phonation ratio can reflect the severity of MS.

Acoustical and Perceptual Analysis of Voice in Individuals with Parkinson's Disease

Parkinson's disease is a neurodegenerative disorder that affects motor efficiency which is also required for voice production. Voice is part of the identity of a person, any abnormality in voice quality hampers the quality of communication, and quality of life. This study aimed to analyse the voice of individuals with Parkinson's disease objectively through acoustic analysis, and subjectively through perceptual analysis. Quasi-experimental study conducted in a tertiary health care centre. The study comprised two groups: 12 individuals with Parkinson's disease (11 males and 1 female, mean age: 72.41 years), and 12 healthy controls (10 males and 2 females, mean age: 53.83 years). The voice samples of all the participants were recorded and analyzed using the MDVP software of CSL 4500. The voice samples were analyzed for eleven acoustical parameters, and the perceptual analysis was carried out using the GRBAS scale by two experienced Speech Language Pathologists. Mann-Whitney U test was performed to compare the two groups of participants, and Cronbach's alpha test was performed to find the inter-judge reliability between the perceptual ratings of two listeners. Acoustical comparison showed significant variations in seven parameters (jitter, shimmer, PPQ, APQ, Fatr Hz, Fftr, ATRI), and the perceptual analysis between two listeners showed a fair amount of reliability.

Landmark-based analysis of speech differentiates conversational from clear speech in speakers with muscle tension dysphonia

This study evaluated the feasibility of differentiating conversational and clear speech produced by individuals with muscle tension dysphonia (MTD) using landmark-based analysis of speech (LMBAS). Thirty-four adult speakers with MTD recorded conversational and clear speech, with 27 of them able to produce clear speech. The recordings of these individuals were analyzed with the open-source LMBAS program, SpeechMark®, matlab Toolbox version 1.1.2. The results indicated that glottal landmarks, burst onset landmarks, and the duration between glottal landmarks differentiated conversational speech from clear speech. LMBAS shows potential as an approach for detecting the difference between conversational and clear speech in dysphonic individuals.

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.

Using a smartwatch and smartphone to assess early Parkinson's disease in the WATCH-PD study

Digital health technologies can provide continuous monitoring and objective, real-world measures of Parkinson's disease (PD), but have primarily been evaluated in small, single-site studies. In this 12-month, multicenter observational study, we evaluated whether a smartwatch and smartphone application could measure features of early PD. 82 individuals with early, untreated PD and 50 age-matched controls wore research-grade sensors, a smartwatch, and a smartphone while performing standardized assessments in the clinic. At home, participants wore the smartwatch for seven days after each clinic visit and completed motor, speech and cognitive tasks on the smartphone every other week. Features derived from the devices, particularly arm swing, the proportion of time with tremor, and finger tapping, differed significantly between individuals with early PD and age-matched controls and had variable correlation with traditional assessments. Longitudinal assessments will inform the value of these digital measures for use in future clinical trials.

Aberrant neurophysiological signaling associated with speech impairments in Parkinson's disease

Difficulty producing intelligible speech is a debilitating symptom of Parkinson's disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. Using task-free magnetoencephalography, we examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N = 59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N = 65), we show that articulation impairments in patients with PD are associated with aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.

Early detection of Parkinson disease using stacking ensemble method

Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures.

The Effects of Speech Task on Lexical Stress in Parkinson's Disease

PURPOSE

Hypokinetic dysarthria associated with Parkinson's disease (PD) is characterized by dysprosody, yet the literature is mixed with respect to how dysprosody affects the ability to mark lexical stress, possibly due to differences in speech tasks used to assess lexical stress. The purpose of this study was to compare how people with and without PD modulate acoustic dimensions of lexical stress-fundamental frequency, intensity, and duration-to mark lexical stress across three different speech tasks.

METHOD

Twelve individuals with mild-to-moderate idiopathic PD and 12 age- and sex-matched older adult controls completed three speech tasks: picture description, word production in isolation, and word production in lists. Outcome measures were the fundamental frequency, intensity, and duration of the vocalic segments of two trochees (initial stress) and two iambs (final stress) spoken in all three tasks.

RESULTS

There were very few group differences. Both groups marked trochees by modulating intensity and fundamental frequency and iambs by modulating duration. Task had a significant impact on the stress patterns used by both groups. Stress patterns were most differentiated in words produced in isolation and least differentiated in lists of words.

CONCLUSIONS

People with PD did not demonstrate impairments in the production of lexical stress, suggesting that dysprosody associated with PD does not impact all types of prosody in the same way. However, there were reduced distinctions in stress marking that were more apparent in trochees than iambs. In addition, the task used to assess prosody has a significant effect on all acoustic measures. Future research should focus on the use of connected speech tasks to obtain more generalizable measures of prosody in PD.

Acoustic Analysis of Intonation in Persons With Parkinson's Disease Receiving Transcranial Magnetic Stimulation and Intensive Voice Treatment

Intonation is one of the prosodic features manifested acoustically in the fundamental frequency (f0). Intonation abnormality is common and prominent in the speech of persons with Parkinson's disease (PD). The current research investigated acoustically five intonational features including f0 declination, f0 resetting, sentence stress, terminal fall, and syntactic prejunctural fall in 20 PD participants, receiving Lee Silverman Voice Treatment (LSVT)-LOUD alone, or combined with transcranial magnetic stimulation delivered to the left or right primary laryngeal motor cortex. The results revealed that f0 declination, sentence stress, and terminal fall changed significantly from pre- to post-treatment, and the changes of declination and terminal fall were maintained at the follow-up evaluations. The observed changes in intonation were attributed to LSVT alone, which caused large changes of f0 magnitude. f0 resetting and syntactic prejunctural fall did not change significantly following treatment, probably because these intonational features need very precise fine motor control of the intrinsic laryngeal muscles to make small-range, rapid f0 adjustments, which were not improved by LSVT in the present PD participants. Difficulties with syntactic processing previously reported in PD may have also contributed to the lack of improvement in resetting and prejunctural fall, since these f0 features are used to mark syntactic boundaries within utterances.

Investigating the Impact of Voice Impairment on Quality of Life in Stroke Patients: The Voice Handicap Index (VHI) Questionnaire Study

The Voice Handicap Index (VHI) is a patient-centered evaluation tool specifically designed for assessing voice-related quality of life. Although the VHI has been extensively used in patients with voice disorders, its applicability in stroke patients has not been fully established. This prospective cross-sectional study aimed to investigate the feasibility of using the VHI questionnaire in identifying stroke patients with voice problems. The study included a cohort of acute to subacute first-ever stroke patients (n = 48), with or without voice problems, as well as other non-stroke patients (n = 31) who agreed to complete the VHI questionnaire. Stroke patients with self-reported voice problems demonstrated significantly higher VHI scores and poorer life quality scores compared to the control groups. These patients also had lower Mini-Mental State Examination (MMSE), Modified Barthel Index (MBI), and Euro-QoL-5D-5L (EQ-5D-5L) scores. Spearman correlation analysis revealed an inverse association between VHI scores and EQ-5D-5L (rho = -0.77, p < 0.001), Korean Mann Assessment of Swallowing Ability (rho = -0.51, p < 0.001), and other functional parameters, including the National Institutes of Health Stroke Scale, MMSE, and MBI scores. Multiple regression analysis indicated that the VHI score was the biggest contributing factor to EQ scores. This is the first study to demonstrate that stroke patients with voice problems may experience reduced quality of life, even after controlling for other confounding factors such as dysphagia or neurological deficits. Future studies are needed whether addressing these issues by implementing the VHI may facilitate the improvement of patients' quality of life.

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.

Speech error rates after a sports-related concussion

Background

Alterations in speech have long been identified as indicators of various neurologic conditions including traumatic brain injury, neurodegenerative diseases, and stroke. The extent to which speech errors occur in milder brain injuries, such as sports-related concussions, is unknown. The present study examined speech error rates in student athletes after a sports-related concussion compared to pre-injury speech performance in order to determine the presence and relevant characteristics of changes in speech production in this less easily detected neurologic condition.

Methods

A within-subjects pre/post-injury design was used. A total of 359 Division I student athletes participated in pre-season baseline speech testing. Of these, 27 athletes (18-22 years) who sustained a concussion also participated in speech testing in the days immediately following diagnosis of concussion. Picture description tasks were utilized to prompt connected speech samples. These samples were recorded and then transcribed for identification of errors and disfluencies. These were coded by two trained raters using a 6-category system that included 14 types of error metrics.

Results

Repeated measures analysis of variance was used to compare the difference in error rates at baseline and post-concussion. Results revealed significant increases in the speech error categories of pauses and time fillers (interjections/fillers). Additionally, regression analysis showed that a different pattern of errors and disfluencies occur after a sports-related concussion (primarily time fillers) compared to pre-injury (primarily pauses).

Conclusion

Results demonstrate that speech error rates increase following even mild head injuries, in particular, sports-related concussion. Furthermore, the speech error patterns driving this increase in speech errors, rate of pauses and interjections, are distinct features of this neurological injury, which is in contrast with more severe injuries that are marked by articulation errors and an overall reduction in verbal output. Future studies should consider speech as a diagnostic tool for concussion.

Differences in spontaneous speech fluency between Parkinson's disease and spinocerebellar ataxia type 3

Background

The basal ganglia and cerebellum both have a role in speech production although the effect of isolated involvement of these structures on speech fluency remains unclear.

Objective

The study aimed to assess the differences in the articulatory pattern in patients with cerebellar vs. basal ganglia disorders.

Methods

A total of 20 individuals with Parkinson's disease (PD), 20 with spinocerebellar ataxia type 3 (SCA3), and 40 controls (control group, CG) were included. Diadochokinesis (DDK) and monolog tasks were collected.

Results

The only variable that distinguished SCA3 carriers from the CG was the number of syllables in the monolog, with SCA3 patients of a significantly lower number. For patients with PD, the number of syllables, phonation time, DDK, and monolog were significantly lower than for CG. Patients with PD were significantly worse compared to patients with SCA3 in the number of syllables and phonation time in DDK, and phonation time in monolog. Additionally, there was a significant correlation between the number of syllables in the monolog and the MDS-UPDRS III for participants with PD, and the Friedreich Ataxia Rating Scale for participants with SCA3 suggesting a relationship between speech and general motor functioning.

Conclusion

The monolog task is better at discriminating individuals with cerebellar vs. Parkinson's diseases as well as differentiating healthy control and was related to the severity of the disease.

Dysarthria in hereditary spastic paraplegia type 4

OBJECTIVE

To describe the speech pattern of patients with hereditary Spastic Paraplegia type 4 (SPG4) and correlated it with their clinical data.

METHODS

Cross-sectional study was carried out in two university hospitals in Brazil. Two groups participated in the study: the case group (n = 28) with a confirmed genetic diagnosis for SPG4 and a control group (n = 17) matched for sex and age. The speech assessment of both groups included: speech task recording, acoustic analysis, and auditory-perceptual analysis. In addition, disease severity was assessed with the Spastic Paraplegia Rating Scale (SPRS).

RESULTS

In the auditory-perceptual analysis, 53.5% (n = 15) of individuals with SPG4 were dysarthric, with mild to moderate changes in the subsystems of phonation and articulation. On acoustic analysis, SPG4 subjects' performances were worse in measurements related to breathing (maximum phonation time) and articulation (speech rate, articulation rate). The articulation variables (speech rate, articulation rate) are related to the age of onset of the first motor symptom.

CONCLUSION

Dysarthria in SPG4 is frequent and mild, and it did not evolve in conjunction with more advanced motor diseases. This data suggest that diagnosed patients should be screened and referred for speech therapy evaluation and those pathophysiological mechanisms of speech involvement may differ from the length-dependent degeneration of the corticospinal tract.

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.

Acoustic Voice Analysis of Normal and Pathological Voices in Indian Population Using Praat Software

Acoustic voice analysis is still a valuable technique which enables voice clinicians to compare voices to differentiate them into normal and abnormal. The present study was undertaken to standardize acoustic voice parameters in normal healthy adult individuals and gender comparison among them and also acoustic voice analysis of pathological voices and it's comparison with normal healthy voices. Voice samples of vowels /a/, /i/ and /u/ of 80 normal healthy adults (males = 40, females = 40) of control group and 40 patients with dysphonic voice of case group collected and acoustic voice parameters were extracted by using Praat software. There were statistically significant higher values of fundamental frequency (F0) in females, while jitter local (%), shimmer local (%) and harmonic to noise ratio (HNR) had no gender differences in normal healthy voices. Pathological voices of case group subjects with laryngeal pathologies had statistically significant higher values of jitter local (%), shimmer local (%) and lower values of HNR as compare to normal healthy voices of control group. Objective voice analysis by using Praat software is convenient, reliable and cost effective method. This study establishes normative acoustic voice parameters in normal healthy adults. There are no gender differences in adult healthy voices except fundamental frequency (F0), which is higher in females. Patients who are with dysphonic voices due to laryngeal pathologies had altered values of acoustic parameters compared to normophonic adults and clinicians can precisely differentiate pathological voices from normophonics.

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.

Computerized analysis of speech and voice for Parkinson's disease: A systematic review

BACKGROUND AND OBJECTIVE

Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity.

METHODS

A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed.

RESULTS

Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians.

CONCLUSIONS

The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.

An integrated biometric voice and facial features for early detection of Parkinson’s disease

Hypomimia and voice changes are soft signs preceding classical motor disability in patients with Parkinson's disease (PD). We aim to investigate whether an analysis of acoustic and facial expressions with machine-learning algorithms assist early identification of patients with PD. We recruited 371 participants, including a training cohort (112 PD patients during "on" phase, 111 controls) and a validation cohort (74 PD patients during "off" phase, 74 controls). All participants underwent a smartphone-based, simultaneous recording of voice and facial expressions, while reading an article. Nine different machine learning classifiers were applied. We observed that integrated facial and voice features could discriminate early-stage PD patients from controls with an area under the receiver operating characteristic (AUROC) diagnostic value of 0.85. In the validation cohort, the optimal diagnostic value (0.90) maintained. We concluded that integrated biometric features of voice and facial expressions could assist the identification of early-stage PD patients from aged controls.

Speech and language characteristics differentiate Alzheimer's disease and dementia with Lewy bodies

Introduction

Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important, but it remains challenging. Different profiles of speech and language impairments between AD and DLB have been suggested, but direct comparisons have not been investigated.

Methods

We collected speech responses from 121 older adults comprising AD, DLB, and cognitively normal (CN) groups and investigated their acoustic, prosodic, and linguistic features.

Results

The AD group showed larger differences from the CN group than the DLB group in linguistic features, while the DLB group showed larger differences in prosodic and acoustic features. Machine-learning classifiers using these speech features achieved 87.0% accuracy for AD versus CN, 93.2% for DLB versus CN, and 87.4% for AD versus DLB.

Discussion

Our findings indicate the discriminative differences in speech features in AD and DLB and the feasibility of using these features in combination as a screening tool for identifying/differentiating AD and DLB.

Reliability and validity of the Roche PD Mobile Application for remote monitoring of early Parkinson's disease

Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

Classification of Dysphonic Voices in Parkinson's Disease with Semi-Supervised Competitive Learning Algorithm

This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson’s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann−Whitney−Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.

Study protocol for using a smartphone application to investigate speech biomarkers of Parkinson's disease and other synucleinopathies: SMARTSPEECH

INTRODUCTION

Early identification of Parkinson's disease (PD) in its prodromal stage has fundamental implications for the future development of neuroprotective therapies. However, no sufficiently accurate biomarkers of prodromal PD are currently available to facilitate early identification. The vocal assessment of patients with isolated rapid eye movement sleep behaviour disorder (iRBD) and PD appears to have intriguing potential as a diagnostic and progressive biomarker of PD and related synucleinopathies.

METHODS AND ANALYSIS

Speech patterns in the spontaneous speech of iRBD, early PD and control participants' voice calls will be collected from data acquired via a developed smartphone application over a period of 2 years. A significant increase in several aspects of PD-related speech disorders is expected, and is anticipated to reflect the underlying neurodegeneration processes.

ETHICS AND DISSEMINATION

The study has been approved by the Ethics Committee of the General University Hospital in Prague, Czech Republic and all the participants will provide written, informed consent prior to their inclusion in the research. The application satisfies the General Data Protection Regulation law requirements of the European Union. The study findings will be published in peer-reviewed journals and presented at international scientific conferences.

Phonemes based detection of parkinson's disease for telehealth applications

Dysarthria is an early symptom of Parkinson’s disease (PD) which has been proposed for detection and monitoring of the disease with potential for telehealth. However, with inherent differences between voices of different people, computerized analysis have not demonstrated high performance that is consistent for different datasets. The aim of this study was to improve the performance in detecting PD voices and test this with different datasets. This study has investigated the effectiveness of three groups of phoneme parameters, i.e. voice intensity variation, perturbation of glottal vibration, and apparent vocal tract length (VTL) for differentiating people with PD from healthy subjects using two public databases. The parameters were extracted from five sustained phonemes; /a/, /e/, /i/, /o/, and /u/, recorded from 50 PD patients and 50 healthy subjects of PC-GITA dataset. The features were statistically investigated, and then classified using Support Vector Machine (SVM). This was repeated on Viswanathan dataset with smartphone-based recordings of /a/, /o/, and /m/ of 24 PD and 22 age-matched healthy people. VTL parameters gave the highest difference between voices of people with PD and healthy subjects; classification accuracy with the five vowels of PC-GITA dataset was 84.3% while the accuracy for other features was between 54% and 69.2%. The accuracy for Viswanathan’s dataset was 96.0%. This study has demonstrated that VTL obtained from the recording of phonemes using smartphone can accurately identify people with PD. The analysis was fully computerized and automated, and this has the potential for telehealth diagnosis for PD.

Prosodic features in Finnish-speaking adults with Parkinson´s disease

The aim of this study was to assess prosodic features in Finnish speakers with (n = 16) and without (n = 20) Parkinson's disease (PD), as there are no published studies to date of prosodic features in Finnish speakers with PD. Chosen metrics were articulation rate (syllables/second), pitch (mean F₀) and pitch variability (standard deviation F₀), energy proportion below 1 kHz (epb1kHz), normalised pairwise variability index (nPVI), and a novel syllabic prosody index (SPI). Four statistically significant results were found: (1) energy was distributed more to lower frequencies in speakers with PD compared to control speakers, (2) male PD speakers had higher pitch and (3) higher syllabic prosody index compared to control males, and (4) female PD speakers had narrower pitch variability than controls. In this study, PD was manifested as less emphatic and breathier voice. Interestingly, male PD speakers' dysprosody was manifested as an effortful speaking style, whereas female PD speakers exhibited dysprosody with a monotonous speaking style. A novel syllable-based prosody index could be a potentially useful tool in analysing prosody in disordered speech.

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.

Characterization of Mild and Moderate Dysarthria in Parkinson’s Disease: Behavioral Measures and Neural Correlates

Purpose

Alterations in speech and voice are among the most common symptoms in Parkinson’s disease (PD), often resulting in motor speech disorders such as hypokinetic dysarthria. We investigated dysarthria, verbal fluency, executive functions, and global cognitive function in relation to structural and resting-state brain changes in people with PD.

Methods

Participants with mild-moderate PD (n = 83) were recruited within a randomized controlled trial and divided into groups with varying degrees of dysarthria: no dysarthria (noDPD), mild dysarthria (mildDPD), moderate dysarthria (modDPD), and also combined mildDPD and modDPD into one group (totDPD). Voice sound level and dysphonia, verbal fluency, motor symptoms, executive functions, disease severity, global cognition, and neuroimaging were compared between groups. Gray matter volume and intensity of spontaneous brain activity were analyzed. Additionally, regressions between behavioral and neuroimaging data were performed.

Results

The groups differed significantly in mean voice sound level, dysphonia, and motor symptom severity. Comparing different severity levels of dysarthria to noDPD, groups differed focally in resting-state activity, but not in brain structure. In totDPD, lower scores on semantic verbal fluency, a composite score of executive functions, and global cognition correlated with lower superior temporal gyrus volume.

Conclusion

This study shows that severity of dysarthria may be related to underlying structural and resting-state brain alterations in PD as well as behavioral changes. Further, the superior temporal gyrus may play an important role in executive functions, language, and global cognition in people with PD and dysarthria.

Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias

PURPOSE

This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls.

METHOD

Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their performances were compared to a manual transcript used as a reference.

RESULTS

The Google & Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r > .87 for monologues and r > .91 for reading passages, p < .001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r > .65 for monologues and r > .78 for reading passages, p < .001). Compared to the control group, we detected a slow rate in patients with MSA and a tendency toward a slower rate in patients with iRBD, whereas the articulation rate was unchanged in patients with early untreated PD.

CONCLUSIONS

The state-of-the-art speech recognition tool provided the most precise articulation rate estimates. If speech recognizer is not accessible, the freely available Praat script based on simple intensity thresholding might still provide robust properties even in severe dysarthria. Automated articulation rate assessment may serve as a natural, inexpensive biomarker for monitoring disease severity and a differential diagnosis of Parkinsonism.

Voice in Parkinson's Disease: A Machine Learning Study

Introduction

Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy.

Methods

We investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations.

Results

Voice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning.

Conclusion

Voice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.