Ahmed M Yousef, Emma C Willis, Vahni Tagirisa, Robert E Hillman, Daryush D Mehta
{"title":"Measuring Vocal Efficiency in Daily Life in Patients with Voice Disorders Using Wireless Accelerometer and Microphone Sensors.","authors":"Ahmed M Yousef, Emma C Willis, Vahni Tagirisa, Robert E Hillman, Daryush D Mehta","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Voice disorders are linked to inefficient phonation, with vocal efficiency (VE) measured in clinic. We studied ecological vocal efficiency (EVE) by estimating daily-life VE as SPL/Ps (dB/dB) using a wireless dual-sensor voice monitor: subglottal pressure (Ps) predicted from a neck-surface accelerometer (ACC) and sound pressure level (SPL) from a microphone. 14 females participated (7 with vocal fold nodules; 7 matched controls). In lab, VE was computed via a /p/-vowel sequence (loud-to-soft; 3 pitch levels). Participants completed 3 days of ambulatory monitoring (12 h/day), and EVE was computed. Patients showed lower EVE and reduced variability than controls in daily life (4.1 vs 4.7, effect size r = 0.67; SD 0.4 vs 1.0, r = 0.91); variability metrics separated groups better in field than in lab. Patients spent significantly more time outside the EVE normative range than controls (92 vs 36%; p = 0.03; r = 0.81). Preliminary results support EVE as an ecological marker of vocal function.</p>","PeriodicalId":521241,"journal":{"name":"Interspeech","volume":"2026 ","pages":"5134-5138"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shrishail Baligar, Ahmed M Yousef, Daryush D Mehta
{"title":"Toward an Articulatory Weakness Index for Speech Kinematics in Parkinson's Disease.","authors":"Shrishail Baligar, Ahmed M Yousef, Daryush D Mehta","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Many acoustic speech metrics often reflect a combination of respiratory, laryngeal, and articulatory contributions, thereby preventing direct measurement of articulatory dynamics. We have developed an articulatory weakness index (AWI) derived from electromagnetic articulography (EMA) or acoustic data. We learn an articulatory-state coordinate <math> <msub><mrow><mi>Z</mi></mrow> <mrow><mi>a</mi></mrow> </msub> </math> from healthy EMA data, where higher values indicate restricted articulator motion. In cohorts of individuals with Parkinson's disease (PD) and controls, AWI is elevated in PD and correlates with motor severity, while remaining stable across in healthy controls and speakers with vocal hyperfunction. AWI provides an interpretable, disentangled articulatory weakness index from acoustic speech recordings, complementing respiratory and laryngeal measures.</p>","PeriodicalId":521241,"journal":{"name":"Interspeech","volume":"2026 ","pages":"2152-2156"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149198755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
InterspeechPub Date : 2024-09-01DOI: 10.21437/interspeech.2024-1422
Bao Hoang, Yijiang Pang, Hiroko Dodge, Jiayu Zhou
{"title":"Translingual Language Markers for Cognitive Assessment from Spontaneous Speech.","authors":"Bao Hoang, Yijiang Pang, Hiroko Dodge, Jiayu Zhou","doi":"10.21437/interspeech.2024-1422","DOIUrl":"10.21437/interspeech.2024-1422","url":null,"abstract":"<p><p>Mild Cognitive Impairment (MCI) is considered a prodromal stage of dementia, including Alzheimer's disease. It is characterized by behavioral changes and decreased cognitive function, while individuals can still maintain their independence. Early detection of MCI is critical, as it allows for timely intervention, enrichment of clinical trial cohorts, and the development of therapeutic approaches. Recently, language markers have been shown to be a promising approach to identifying MCI in a non-intrusive, affordable, and accessible fashion. In the InterSpeech 2024 TAUKADIAL Challenge, we study language markers from spontaneous speech in English and Chinese and use the bilingual language markers to identify MCI cases and predict the Mini-Mental Status Examination (MMSE) scores. Our proposed framework combines the power from 1) feature extraction of a comprehensive set of bilingual acoustic features, and semantic and syntactic features from language models; 2) careful treatment of model complexity for small sample size; 3) consideration of imbalanced demographic structure, potential outlier removal, and a multi-task treatment that uses the prediction of clinical classification as prior for MMSE prediction. The proposed approach delivers an average of 78.2% Balanced Accuracy in MCI detection and an averaged RMSE of 2.705 in predicting MMSE. Our empirical evaluation shows that translingual language markers can improve the detection of MCI from spontaneous speech. Our codes are provided in https://github.com/illidanlab/translingual-language-markers.</p>","PeriodicalId":521241,"journal":{"name":"Interspeech","volume":"2024 ","pages":"977-981"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885109/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}