Speech, Facial and Fine Motor Features for Conversation-Based Remote Assessment and Monitoring of Parkinson's Disease

Kothare, Hardik; Roesler, Oliver; Burke, William J.; Neumann, Michael; Liscombe, Jackson; Exner, Andrew; Snyder, Sandy; Cornish, Andrew; Habberstad, Doug; Pautler, David; Suendermann-Oeft, David; Huber, Jessica E.; Ramanarayanan, Vikram

doi:10.1109/embc48229.2022.9871375

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Concerning the generalizability of the presented findings to other neurological or mental disorders, it is important to note that the specific set of selected useful features, including the relative utility of different modalities, will likely be different in other disorders (cf. related work in Parkinson disease [73] or mental health disorders like depression [74] and schizophrenia [75]), as well as the sensitivity analysis with respect to a traditional clinical outcome (which is dependent on the disease). However, the methods that were applied in this study can be readily transferred to other diseases.…”

Section: Discussionmentioning

confidence: 99%

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression

Neumann,

Kothare,

Ramanarayanan

2024

Preprint

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

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Section: Discussionmentioning

confidence: 99%

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression

Neumann,

Kothare,

Ramanarayanan

2024

Preprint

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Vocal and Facial Behavior During Affect Production in Autism Spectrum Disorder

Kothare,

Ramanarayanan,

Neumann

et al. 2024

J Speech Lang Hear Res

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Purpose: We investigate the extent to which automated audiovisual metrics extracted during an affect production task show statistically significant differences between a cohort of children diagnosed with autism spectrum disorder (ASD) and typically developing controls. Method: Forty children with ASD and 21 neurotypical controls interacted with a multimodal conversational platform with a virtual agent, Tina, who guided them through tasks prompting facial and vocal communication of four emotions—happy, angry, sad, and afraid—under conditions of high and low verbal and social cognitive task demands. Results: Individuals with ASD exhibited greater standard deviation of the fundamental frequency of the voice with the minima and maxima of the pitch contour occurring at an earlier time point as compared to controls. The intensity and voice quality of emotional speech were also different between the two cohorts in certain conditions. Additionally, facial metrics capturing the acceleration of the lower lip, lip width, eye opening, and vertical displacement of the eyebrows were also important markers to distinguish between children with ASD and neurotypical controls. Both facial and speech metrics performed well above chance in group classification accuracy. Conclusion: Speech acoustic and facial metrics associated with affect production were effective in distinguishing between children with ASD and neurotypical controls. Supplemental Material: https://doi.org/10.23641/asha.28027796

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Speech, Facial and Fine Motor Features for Conversation-Based Remote Assessment and Monitoring of Parkinson's Disease

Cited by 2 publications

References 22 publications

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression

Multimodal Speech Biomarkers for Remote Monitoring of ALS Disease Progression

Vocal and Facial Behavior During Affect Production in Autism Spectrum Disorder

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