The paper examines why speech‑based screening for Alzheimer’s disease fails to generalize across different languages, tasks, and recording protocols. Using a leave‑one‑corpus‑out evaluation on four datasets, it finds that 59 of 70 interpretable speech features show conflicting patterns between healthy controls and cognitive risk groups, with pause, silence, and speech rate being highly protocol‑sensitive. The authors propose a fusion method that combines XLM‑R text baseline scores with evidence anchors, improving mean speaker AUC to 0.785 and worst‑case AUC to 0.615, and emphasize the importance of auditing feature transferability and reporting worst‑case domain robustness.
By Zijian Lu, Sizhe Liu, Yin Zhang, Jixuan Deng, Xinrong Lin, Xinchen Yuan, Chicheng Jin, Yiping Zuo, Yuanchao Li
arXiv:2606. 18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities.
By William Nguyen, Jiali Cheng, Hadi Amiri
The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and time-consuming nature of language-specific model training. We propose a novel solution using cross-language training to detect AD in languages beyond those used for model training.
arXiv:2606. 30646v1 Announce Type: cross Abstract: Speech recruits the same executive, attentional, and working memory processes underlying instrumental activities of daily living, or IADLs, providing a non-invasive proxy for cognitive assessment.
By Chukwuemeka Ugwu, Oluwafemi Richard Oyeleke
arXiv:2606. 26901v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diverse Indian healthcare context remains largely unknown.
By Subham Kumar, Prakrithi Shivaprakash, Abhishek Manoharan, Astut Kurariya, Diptadhi Mukherjee, Prabhat Chand, Pratima Murthy, Koustav Rudra, Lekhansh Shukla, Animesh Mukherjee
arXiv:2609.18533v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representation...
By Nicolas Bourrel, Abderrahmane Issam, Gerasimos Spanakis
arXiv:2607. 23977v1 Announce Type: cross Abstract: Acoustic biomarkers show promise for detecting Alzheimer's Disease (AD), yet whether the cues driving diagnostic AI align with those salient to human listeners is underexplored across languages and genders, where pathological markers and perceptual strategies differ.
By Liu He, Yuanchao Li, Yin-Long Liu, Rui Feng, Yiming Wang, Jiaxin Chen, Yizhe Wang, Jiahong Yuan
Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representations. We ask whether speaker-linked attributes that...
arXiv:2511.07011v2 Announce Type: replace-cross
Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research t...
By Anastasiia Tokareva, Judith Dineley, Zoe Firth, Pauline Conde, Faith Matcham, Sara Siddi, Femke Lamers, Ewan Carr, Carolin Oetzmann, Daniel Leightley, Yuezhou Zhang, Amos A. Folarin, Josep Maria Haro, Brenda W. J. H. Penninx, Raquel Bailon, Srinivasan Vairavan, Til Wykes, Richard J. B. Dobson, Vaibhav A. Narayan, Matthew Hotopf, Nicholas Cummins, The RADAR-CNS Consortium
The paper examines how automatic speech recognition (ASR) systems perform on Newcastle English, a regional dialect of North‑East England, using spontaneous speech from the Diachronic Electronic Corpus of Tyneside English. It analyzes over 3,000 transcription errors, classifying them by linguistic domain and social variables such as gender, age, and socioeconomic status, and highlights that phonological variation—especially vowel quality and glottalisation—drives most errors. The study finds that error rates differ across social groups, with men and speakers at age extremes experiencing higher rates, indicating that ASR errors are socially patterned rather than random.
By Dana Serditova, Kevin Tang
The study examines how multilingual large language models (LLMs) produce outputs that differ across sociocultural contexts, highlighting that identity labels and source-language cues can mislead assessments of cultural grounding. Using a human‑validated, multi‑agent audit on 89,253 outputs from 12 LLMs in English, French, and Chinese across 18 occupations and three task conditions, the authors find that bias representation varies systematically by language and task. Removing direct identity cues reduces identity‑label prediction in English and Chinese but not in French, and the source language’s cultural context consistently receives the highest relevance score, though this signal weakens after translation or name masking.
"whyItMatters":"The findings show that surface cues can obscure true cross‑cultural patterns, underscoring the need for careful audit designs to avoid misleading conclusions about bias in multilingual LLMs."
By Yuanjun Feng, Tanzhou Liu, Stefan Feuerriegel, Yash Raj Shrestha
The paper introduces a contactless, multimodal screening framework that uses video, audio, and text data collected during structured voice interactions with Amazon Alexa to detect mild cognitive impairment (MCI). It evaluates three conversational tasks—spontaneous speech, comprehension, and recall—to determine which provides the most informative signals, compares unimodal models, and assesses multimodal fusion. Results show that while individual modalities perform best on specific tasks, the multimodal model consistently achieves higher accuracy (up to 0.76) across tasks, indicating that integrated behavioral cues can reliably distinguish MCI from healthy aging.
By Sankalp Vaish, Rishank Singh, Sumukh Veeramalla, Caroline Summerour, John A. Batsis, Xiaohui Liang