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.
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...
arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.
arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.
arXiv:2606. 18645v1 Announce Type: cross Abstract: Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness.
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....
The study evaluates a pipeline for detecting dysarthria during awake craniotomy using the DATABRASE corpus. It combines speaker diarization, a multi‑view representation of handcrafted acoustic features and wav2vec 2.0 embeddings, speaker‑conditional normalization, feature selection, and a cascaded classifier. Results show that the speech representation has a larger impact on cross‑speaker performance than classifier choice, with self‑supervised embeddings improving AUC by 18.2%–26.1% and additional gains from diarization‑conditioned extraction and the classifier cascade.
arXiv:2607. 21496v1 Announce Type: cross Abstract: Cognitive impairment (CI) is a growing public health concern.
The paper introduces SITAR, a situational distortion metric for evaluating automatic speech recognition (ASR) systems. SITAR aggregates situational severity and cumulative extent into a unified score using a bounded recursive formulation, and is implemented as a lightweight BERT-family regressor that requires minimal runtime cost. The authors validate SITAR through extrinsic and intrinsic protocols, showing it tracks capability scaling and provides diagnostic utility across diverse datasets and models.
The study evaluates seven parameter‑efficient fine‑tuning (PEFT) methods—LoRA, QLoRA, AdaLoRA, DoRA, LoHA, VeRA, and VB‑LoRA—on two ASR back‑ends (Whisper‑large‑v3 and Qwen3‑ASR‑1.7B) for a single post‑stroke Hungarian male speaker with severe dysarthria. Attention‑projection adapters consistently lower character error rates (CER) on both models, with LoRA emerging as the simplest and most effective choice; QLoRA performs worse and offers no memory advantage at this scale. Full fine‑tuning yields the lowest CER, but a 115 MB LoRA that also adapts feed‑forward blocks achieves comparable accuracy with only 3.7 % of the per‑patient storage, and a 5‑minute enrollment audio captures nearly half of the zero‑shot‑to‑30‑minute CER improvement. whyItMatters":"The paper demonstrates that lightweight PEFT adapters can substantially improve dysarthric ASR performance while keeping storage and computational costs low, offering a practical path for personalized speech recognition in clinical settings."
arXiv:2606. 19791v1 Announce Type: cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision.
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.
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.