32 citations · 36 across the 12 of their papers we have counts for
7 papers · 1 filter
Qwen-Audio-3.1-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +45
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 sp…
WenetSpeech-Min: A Large-Scale Minnan Speech Corpus with Dual Transcriptions for Dialectal Speech Processing
Haoyu Zhang, Chunjiang He, Hongtao Li +17
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…
Multimodal Conversational Context for LLM-Based ASR: Data Construction, Training, and Benchmark
Longhao Li, Jian Tang, Yuxiang Kong +4
Conversational context provides semantic and acoustic cues across turns for automatic speech recognition (ASR), but relying on historical transcripts can propagate recognition erro…
Qwen-Audio-3.0-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +42
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integra…
On-Policy Self-Distillation for Multi-Dialect ASR: Mastering Dialects, Retaining Mandarin
Shuiyuan Wang, Bingshen Mu, Pengshen Zhang +6
Recent large-scale ASR models already achieve strong Mandarin recognition accuracy and have some ability to recognize Chinese dialects. However, their dialect recognition accuracy…
GigaSpeechBench: A Real-World Multilingual Speech-to-Text Benchmark
Yujie Tu, Yifan Yang, Tianrui Wang +35
While modern ASR systems achieve low error rates on high-resource benchmarks, such performance often overestimates real-world robustness. Existing evaluations address challenges in…
WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem
Chengyou Wang, Mingchen Shao, Jingbin Hu +11
Speech processing for low-resource dialects remains a fundamental challenge in developing inclusive and robust speech technologies. Despite its linguistic significance and large sp…