We present Qwen-Audio-3.1-ASR, a family of audio-language models that builds upon its predecesor Qwen-Audio-3.0-ASR with improved speech recognition and expanded capabilities in speaker-diarized transcription and general audio understanding. The family retains support for 30 languages and 16 Chinese dialects, together...
Chuan-Meng Bian, Da-Ren Chen, Pei-Xin Chen et al.· 0 citations
Results demonstrate that explicitly coupling count- and boundary-aware revision with joint reward optimization provides an effective and verifiable path to temporal audio grounding.
Yu-Hang Dai, Xin Shu, Zhen-Nan Lin et al.· 0 citations
Large audio language models (LALMs) can describe what is heard, but their ability to localize when queried content occurs remains less systematically evaluated. We present TAG-Bench, a benchmark for temporal audio grounding in which a model returns every time interval that matches a natural-language query. TAG-Bench co...
Yu-Hang Dai, Xin Shu, Zeng-Xi Li et al.· 2 citations
Qwen-Audio-3.0-ASR is presented, a Mixture-of-Experts (MoE) LLM-based ASR system designed to address production demands through a unified, instruction-following framework and demonstrates state-of-the-art or highly competitive recognition performance across a broad range of evaluation conditions.
Chuan-Meng Bian, Da-Ren Chen, Pei-Xin Chen et al.· 3 citations
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