arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.
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...
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:2606. 18645v1 Announce Type: cross Abstract: Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness.
arXiv:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.
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....
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 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. 27543v1 Announce Type: cross Abstract: The variations in vocal effort range (e.
Individuals with dysarthria face significant challenges in professional speaking scenarios such as conferences, presentations, and meetings, where real-time communication is crucial. While existing Augmentative and Alternative Communication (AAC) systems provide basic support, they often fail to meet the demands of professional speaking environments due to high latency and unnatural speech patterns.
arXiv:2607. 17615v1 Announce Type: cross Abstract: Individuals with dysarthria face significant challenges in professional speaking scenarios such as conferences, presentations, and meetings, where real-time communication is crucial.
arXiv:2606. 19793v1 Announce Type: cross Abstract: The challenge associated with recognizing dysarthric speech primarily arises from pronounced acoustic variability attributed to impaired articulatory precision.
Although Whisper models benefit from large-scale multilingual pre-training, their performance on Burmese medical speech remains limited. This work presents a Burmese medical speech recognition framework built on a high-quality 28-hour corpus recorded and validated by native speakers.