BenSparX introduces the first Bengali conversational speech dataset for Parkinson’s disease detection and pairs it with a robust, explainable machine learning framework. The framework uses diverse acoustic features, systematic feature selection, and advanced classifiers, achieving 95.67% accuracy, 95.62% F1, and 0.990 AUC. SHAP analysis is employed to explain feature contributions, and the model outperforms state‑of‑the‑art methods on other language datasets.
By Riad Hossain, Muhammad Ashad Kabir, Arat Ibne Golam Mowla, Animesh Chandra Roy, Ranjit Kumar Ghosh
arXiv:2510.22172v2 Announce Type: replace-cross
Abstract: The Continuous Integrate-and-Fire (CIF) mechanism provides effective alignment for non-autoregressive (NAR) speech recognition. This mechanis...
By Ruixiang Mao, Xiangnan Ma, Qing Yang, Ziming Zhu, Yucheng Qiao, Yuan Ge, Tong Xiao, Shengxiang Gao, Zhengtao Yu, Jingbo Zhu
The paper evaluates pre‑trained speech embeddings from four state‑of‑the‑art speech foundation models for cross‑lingual Parkinson's disease severity assessment. Experiments span three datasets in zero‑shot and k‑shot settings, showing that these embeddings can transfer meaningfully across languages, though performance varies with dataset characteristics, preprocessing, and adaptation strategy. Misclassifications linked to inter‑speaker variability and atypical speech patterns underscore the need for more robust feature extraction, modeling, and explainability to support reliable clinical insights.
By Simon Pals, Cristian Tejedor-Garcia
The paper evaluates whether audio‑language models can use multimodal clinical context to improve dysarthric speech recognition. Using a benchmark built on the Speech Accessibility Project dataset, the authors test diagnosis labels, clinician ratings, and detailed clinical descriptions as prompts for nine models. They find that these prompts yield negligible or negative effects on word error rate, though fine‑tuning with LoRA and mixed prompt formats reduces WER by 52% and benefits certain subgroups such as Down syndrome and mild‑severity speakers.
By Pehu\'en Moure, Niclas Pokel, Bilal Bounajma, Yingqiang Gao, Roman Boehringer, Longbiao Cheng, Shih-Chii Liu
arXiv:2606. 17339v1 Announce Type: new Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems.
By Sejal Bhalla, Larry Kieu, Aina Merchant, Eyal de Lara, Alex Mariakakis
The paper proposes a linguistically structured multi‑task learning framework for recognizing non‑canonical phonemes by decomposing phoneme prediction into articulatory feature dimensions such as manner, place, and voicing. A hierarchical architecture with task‑specific heads and a cross‑attention fusion module is combined with semi‑supervised Momentum Pseudo‑Labeling and a cascaded training strategy that gradually introduces articulatory tasks. Experiments on the L2‑ARCTIC dataset demonstrate significant improvements over baseline models and produce interpretable error patterns aligned with phonological feature structure.
By Sophia Riaz, Haoze Zheng, Amos Roche, Miyu Zhang, Anamika Ragu, Salvatore Penachio, Kaustav Mukherjee, Aneesh Jonelagadda
The paper introduces two new corpus‑level, reference‑free metrics—Phoneme‑Cluster Mutual Information (PCMI) and Word Acoustic Consistency Score (WACS)—that use self‑supervised speech representations to evaluate forced alignment quality. PCMI quantifies how well aligned phoneme labels agree with clusters derived from SSL representations, while WACS assesses consistency across repeated word realizations via dynamic time warping of word representation sequences. Experiments on 85 languages from FLEURS and 45 languages in DoReCo show that both metrics degrade predictably under alignment perturbations, effectively distinguish high‑ from low‑quality alignments, and correlate strongly with traditional timestamp‑based measures, enabling scalable, multilingual alignment evaluation without manual annotations.
By V. S. D. S. Mahesh Akavarapu, Michael Daniel, Gerhard J\"ager
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.
By Yingchao Huang, Xin Wang, Yuhan Su, Shanshan Yao
arXiv:2609.21789v1 Announce Type: new
Abstract: Most multilingual dysarthria-severity systems either train on a single aetiology-language pair or pool heterogeneous aetiologies into one label space....
By Bernard Muller, Antonio Armando Ortiz Barra\~n\'on, LaVonne Roberts
The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models. By comparing various fine‑tuning strategies—zero‑shot decoding, English‑only, German‑only, two‑stage EN→EN+DE, and direct EN+DE fine‑tuning—it shows that fine‑tuning significantly improves performance, with the best model varying by setting. Layer‑wise encoder analysis reveals that English medical fine‑tuning drives the main representation shift, while multilingual continuation largely preserves the adapted space, and that domain and language signals remain recoverable across layers.
By Souranil Kahali, Rituparna Bose, Abner Hernandez, Tomas Arias-Vergara, Andreas Maier, Ning Ma, Paula Andrea Perez-Toro
arXiv:2607. 23808v1 Announce Type: cross Abstract: In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India.
By Deovrat Mehendale, Aditya Mehndiratta, Dhruv Rathi, Kaushal Bhogale, Mitesh M. Khapra
The paper examines how multilingual medical adaptation affects the internal representations of Whisper ASR models by performing layer‑wise encoder analysis. It compares several adaptation strategies—zero‑shot decoding, English‑only fine‑tuning, German‑only diagnostic fine‑tuning, two‑stage EN→EN+DE continuation, and direct EN+DE fine‑tuning—across different Whisper sizes, finding that fine‑tuning improves performance but the best model varies by setting. Layer‑wise results show that English medical fine‑tuning drives the main encoder shift, while multilingual continuation largely preserves the adapted representation space, with domain and language information remaining recoverable across layers.
"whyItMatters":"The study provides insight into how multilingual medical adaptation reshapes Whisper’s internal representations, guiding the selection of model sizes and fine‑tuning strategies for improved MedASR performance."