Nonverbal vocalizations (NVVs), such as laughter, sighs, breaths, and coughs, convey affective and interactional information that conventional automatic speech recognition (ASR) systems often discard. We present a bilingual Mandarin-English system for Track 1 of the NVVSpeech Challenge at ISCSLP 2026, which requires jo...
Yuang Cao, Qi-Rui Zhan, Jing-Bin Hu et al.· 2 citations
Progress in dialectal speech technology is hindered by the scarcity of large-scale, real-world corpora. For Minnan speech, existing resources remain limited, and few provide paired Minnan and Mandarin transcripts at scale. To address these gaps, we introduce WenetSpeech-Min, an open-source corpus comprising around 10,0...
Hao-Yu Zhang, Chun-Jiang He, Hong-Tao Li et al.· 0 citations
This paper summarizes the ChinaVoices Challenge 2026, which aims to establish unified task definitions and evaluation conditions for Chinese dialect speech processing and to advance multi-dialect identification and automatic speech recognition. The challenge covers 16 dialect categories and defines two tasks: Chinese M...
Yu-Jie Liao, Bing-Shen Mu, Shui-Yuan Wang et al.· 0 citations
This work presents a multimodal conversational-context framework for LLM-based ASR that integrates a scenario-controlled data pipeline, scalable multimodal context training, and systematic evaluation and introduces MM-ContextASR Bench, which evaluates contextual understanding and entity error correction across five sce...
Long-Hao Li, Jian Tang, Yu-Xiang Kong et al.· 0 citations
We introduce the REAL-TSE Challenge, an IEEE SLT 2026 satellite challenge on target speaker extraction~(TSE) from real conversational recordings. Given a multi-speaker mixture and one or more enrollment utterances from a target speaker, participating systems must recover only the target speech. Unlike simulated read-sp...
Shuai Wang, Zihan Qian, Ke Zhang et al.· arXiv.org· 1 citation
DiaScriber is proposed, an end-to-end multi-speaker diarization and transcription model built on a speech large language model that achieves superior performance over comparison methods across extensive multi-speaker scenario test sets and demonstrates outstanding generalization ability in unseen multi-speaker scenario...
Bing-Shen Mu, Xian Shi, Xiong Wang et al.· 1 citation
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