Electrolaryngeal (EL) speech suffers from severe acoustic mismatches with normal speech, resulting in degraded naturalness and intelligibility. These mismatches also make sequence-to-sequence voice conversion struggle to learn robust intermediate representations, hindering EL-speech-to-normal-speech conversion (EL2SP)...
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
The results show that fixed latent reasoning provides a practical, testable route to robust multimodal emotion recognition without requiring missing-modality examples during training.
Chuang-Xin Zhao, Mei-Mei Zhang, Can-Ran Xiao et al.· 0 citations
Large audio language models (LALMs) have made substantial progress in audio understanding. However, autoregressive text generation introduces computational overhead and latency in high-throughput applications that require frequent selection, judgment, and scoring. To address this limitation, we introduce Qwen-Audio-Jev...
Pei-Xin Chen, Zhi-Gao Chen, Zhi-Yun Fan et al.· 0 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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