Discrete audio tokenization has become the critical interface between raw waveforms and autoregressive modeling in recent music generation. As a result, music tokenizers must simultaneously support high-fidelity reconstruction and produce discrete sequences that remain amenable to language modeling. Existing reconstruc...
Hua-Kang Chen, Guo-Bin Ma, Yue-Peng Jiang 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
SemBridge is proposed, a training-only semantic-token anchoring framework for continuous-latent autoregressive speech generation and demonstrates that explicit semantic-token supervision for autoregressive state learning is an effective and general direction for continuous speech generation.
Han-Ke Xie, Haopeng Lin, J. Qian et al.· 0 citations
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