Dynamic speaking style adaptation in multi-turn multimodal interaction remains a major challenge for text-to-speech (TTS) systems. Existing context-aware TTS (CTTS) methods typically map dialogue context to speech in an end-to-end manner. Such implicit modeling makes contextual style decisions difficult to supervise, w...
Wen-Jie Tian, Kang-Xiang Xia, Jing-Bin Hu et al.· 0 citations
A controllable multi-speaker dialogue TTS framework that formulates synthesis as critique-driven iterative refinement, and unifies instruction-following synthesis and natural-language-guided attribute editing, enabling correction of expressive errors without full regeneration.
Kang-Xiang Xia, Xin-Fa Zhu, Hang-Rui Hu et al.· 0 citations
D3-Omni is introduced, a balanced and decoupled benchmark for diagnosing fine-grained multimodal understanding, covering 53 orthogonal binary dimensions and 10,671 samples across the three tasks, suggesting that aggregate accuracy may hide systematic blind spots that a balanced and decoupled lens can help expose and, i...
Guang Hu, Ziyue Jiang, Wei Qiao et al.· 0 citations
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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