An avatar that holds a conversation should decide what to say and to move while saying it, yet these abilities live in separate model families: spoken dialogue models produce speech without motion, and co-speech motion models produce motion only from audio handed to them. The standard remedy is a cascade that first gen...
Chengqian Ma, Wei Tao, Hao-Yu Zhang et al.· 0 citations
With the rapid development of speech generation technology, discrete codec representations have been widely used because they provide a stable prediction paradigm. In expressive speech generation, however, the quantization bottleneck of discrete codecs results in information gaps in fine-grained prosody, timbre, pronun...
Hao-Yu Zhang, Jing-Bin Hu, Han-Ke Xie et al.· 0 citations
Recent advancements in discrete token-based speech generation have highlighted the importance of efficient token-to-waveform synthesis in streaming and dialogue scenarios. Flow-matching acoustic decoders achieve high-quality token-to-mel generation, but their iterative sampling requires multiple neural function evaluat...
Han-Ke Xie, Xia-Ming Ren, Qi-Rui Zhan et al.· 0 citations
Continuous-latent Autoregressive Diffusion Transformer (AR-DiT) models have demonstrated immense potential in zero-shot speech generation. However, they still suffer from limited decoding stability when synthesizing long utterances or complex linguistic structures. This instability primarily stems from a restricted his...
Zi-Yu Zhang, Tian-Lun Zuo, Han-Zhao Li et al.· 0 citations
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