arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.
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arXiv:2606. 18645v1 Announce Type: cross Abstract: Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness.
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.
The study examines how speech preprocessing—such as enhancement, sample selection, and demographic balancing—affects Alzheimer’s disease detection models that use the Pitt Corpus. Experiments reveal that while speech‑enhanced datasets boost in‑domain accuracy, they diminish cross‑dataset robustness and introduce class imbalance and prediction shifts, even when training and testing enhancements are matched. Large audio‑language models show similar sensitivity, indicating that cleaner speech does not guarantee better real‑world performance.
arXiv:2606. 17339v1 Announce Type: new Abstract: Speech offers a uniquely informative window into health by simultaneously engaging neurological, motor, respiratory, and vocal systems.