arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.
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:2603. 15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies.
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.
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: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. 13253v1 Announce Type: cross Abstract: Speech recognition is challenging for dysarthric speakers.
arXiv:2606. 19823v1 Announce Type: cross Abstract: Automatic speech recognition remains unreliable for dysarthric speech due to data scarcity and high inter-speaker variability.
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.
This paper introduces a personalized automatic speech recognition system for a Czech speaker with a permanent tracheal stoma and severe dysarthria. The authors release a 33‑hour annotated dataset collected via an artificial conversation protocol and develop a multi‑stage training pipeline based on Whisper Base, fine‑tuning on Czech speech, simulated tracheostomic speech, and the speaker’s data. Evaluations in scripted, question‑answering, and spontaneous dialogue scenarios show a 50 % relative reduction in character error rate compared to the Whisper Base baseline and better accuracy than the speaker’s assistants on isolated utterances.