arXiv:2606. 16019v1 Announce Type: cross Abstract: Expert phonetic annotation is costly, especially for non-standard dialects and atypical speech.
By Alexander Metzger, Aruna Srivastava, Ruslan Mukhamedvaleev
arXiv:2507.02115v3 Announce Type: cross
Abstract: Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L...
By Zirui Li, Lauri Juvela, Mikko Kurimo
PTC-Bias is a two-stage framework that improves contextual biasing in speech large language models by using phoneme-level temporal competition. In the first stage, PTC Retrieval performs frame-synchronous phoneme decoding to generate a compact shortlist of bias words and their speech intervals. The second stage, PTC Correction, applies a local competition between retrieved candidates and mismatched transcript spans within those intervals, reducing near-homophone and word-segmentation errors without extra SpeechLLM passes. Experiments on LibriSpeech demonstrate consistent gains across two SpeechLLMs, with PTC-Bias reducing B-WER by up to 23.9% relative to CTC-Filter while keeping U-WER nearly unchanged.
By Zhiqi Ai, Han Cheng, Shiyi Mu, Yongjin Zhou, Shugong Xu
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
arXiv:2608.31035v1 Announce Type: new
Abstract: Codec-based text-to-speech (TTS) models make language-model post-training applicable to speech generation, but it remains unclear when learned perceptu...
By Joonyong Park, Jerry Li
The paper investigates whether audio large language models (Audio LLMs) can detect when their own transcriptions are unreliable. It finds that the models are poor at self-assessment and that existing methods offer limited detection. By leveraging audio-encoder representations, the authors develop a lightweight predictor that accurately flags unreliable transcriptions and can prompt user clarification without altering the underlying model.
By Amirhosein Javadi, Richa Dixit, Mehrdad Farajtabar, Minsik Cho, Devang Naik, Mohammad Samragh