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Comprehend and Talk: Text to Speech Synthesis via Dual Language Modeling
Junjie Cao, Yichen Han, Ruonan Zhang +5
Existing Large Language Model (LLM) based autoregressive (AR) text-to-speech (TTS) systems, while achieving state-of-the-art quality, still face critical challenges. The foundation…
MBCodec:Thorough disentangle for high-fidelity audio compression
Ruonan Zhang, Xiaoyang Hao, Yichen Han +3
High-fidelity neural audio codecs in Text-to-speech (TTS) aim to compress speech signals into discrete representations for faithful reconstruction. However, prior approaches faced…
Quantize More, Lose Less: Autoregressive Generation from Residually Quantized Speech Representations
Yichen Han, Xiaoyang Hao, Keming Chen +25
Text-to-speech (TTS) synthesis has seen renewed progress under the discrete modeling paradigm. Existing autoregressive approaches often rely on single-codebook representations, whi…
Towards Lightweight and Stable Zero-shot TTS with Self-distilled Representation Disentanglement
Qianniu Chen, Xiaoyang Hao, Bowen Li +2
Zero-shot Text-To-Speech (TTS) synthesis shows great promise for personalized voice customization through voice cloning. However, current methods for achieving zero-shot TTS heavil…