arXiv Machine Learning October 9, 2026 By Kanthila Chinmayi (IRDL, LaTIM), Abdallah Nassib (LARIS), Misy Harrison (LaTIM), Panheleux Celine (LaTIM, CHU - BREST), Saliou Vanessa (CHU - BREST), Seizeur Romuald (LaTIM), Dardenne Guillaume (LaTIM)

Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

arXiv AI

Aug 20

When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition

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

llms fine-tuning multimodal benchmarks

arXiv Computation and Language

Sep 15

Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation

arXiv:2602.08696v3 Announce Type: replace-cross Abstract: Dysarthric speech recognition is limited by high speaker variability and scarce labeled data. Existing synthesis methods often couple speaker...

By Haoshen Wang, Xueli Zhong, Bingbing Lin, Jia Huang, Xingduo Pan, Shengxiang Liang, Nizhuan Wang, Wai Ting Siok

llms fine-tuning multimodal

arXiv AI

Jun 19

Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation

arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.

By Paban Sapkota, Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Shrikanth Narayanan

fine-tuning multimodal

arXiv AI

Jun 18

Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech

arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.

By Jaesung Bae, Xiuwen Zheng, Minje Kim, Chang D. Yoo, Mark Hasegawa-Johnson

fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 24

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

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

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jun 9

Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention

arXiv:2606. 09271v1 Announce Type: cross Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria.

By George Theodosiou, Loukas Ilias, Dimitris Askounis

rag multimodal benchmarks

One email a morning, machine-written

Related stories

When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition

Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation

Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentation

Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech

Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models

Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention