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: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
arXiv:2606. 18645v1 Announce Type: cross Abstract: Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness.
By Kaimeng Jia, Minzhu Tu, Zengrui Jin, Siyin Wang, Chao Zhang
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
The paper introduces a new target‑speaker unlearning task for automatic speech recognition (TSU‑ASR) that allows certain speakers to opt out of transcription while still indicating their presence. A lightweight Enrollment‑Conditioned Gating (ECG) module is added to a frozen dual‑stream speech LLM, enabling dynamic unlearning of new opt‑out speakers during inference. Experiments on AMI and AliMeeting datasets show significant drops in transcription accuracy for opt‑out speakers while preserving performance for retained speakers.
By Bo Su, Yueru Yan, Thai Le
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
Developers can now build fast speech-to-speech experiences into their applications
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.
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.
By M\'at\'e Gedeon, P\'eter Mihajlik
arXiv:2510. 16834v3 Announce Type: replace-cross Abstract: We present Schr\"odinger Bridge Mamba (SBM), a novel model for efficient speech enhancement by integrating the Schr\"odinger Bridge (SB) training paradigm and the Mamba architecture.
By Jing Yang, Sirui Wang, Chao Wu, Lei Guo, Fan Fan
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