Continuous-latent audio autoencoders form the backbone of latent music generators, yet decoders at high compression rates commonly exhibit three failure modes: high-frequency loss, phase incoherence, and stereo-image collapse. These share a structural root: waveform autoencoders lack an explicit frequency axis, leaving...
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
OmniEchoBench, a unified benchmark for spatial audio-visual perception and audio-vision-language navigation, and a spatially aware omni-modal model, which introduces an FOA spatial encoder alongside a pretrained semantic audio pathway.
MuLA-Bench exposes conditional failure patterns that a single long-context score does not capture, and evaluates ten audio-language models and conducts pooled diagnostics on a fixed eight-model cohort.
Ze-Yu Yang, Xin-Yu Zhang, Zi-Bo Bi et al.· 1 citation
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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