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arXiv · 2511.22687

PURE Codec: Progressive Unfolding of Residual Entropy for Speech Codec Learning

Jiatong Shi·Haoran Wang·William Chen·Chenda Li·Wangyou Zhang·Jinchuan Tian·Shinji Watanabe
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Abstract

Neural speech codecs have achieved strong performance in low-bitrate compression, but residual vector quantization (RVQ) often suffers from unstable training and ineffective decomposition, limiting reconstruction quality and efficiency. We propose PURE Codec (Progressive Unfolding of Residual Entropy), a novel framework that guides multi-stage quantization using a pre-trained speech enhancement model. The first quantization stage reconstructs low-entropy, denoised speech embeddings, while subsequent stages encode residual high-entropy components. This design improves training stability significantly. Experiments demonstrate that PURE consistently outperforms conventional RVQ-based codecs in reconstruction and downstream speech language model-based text-to-speech, particularly under noisy training conditions.

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Jiatong Shi, Haoran Wang, William Chen, Chenda Li, Wangyou Zhang, Jinchuan Tian, Shinji Watanabe. 2025-11-27. PURE Codec: Progressive Unfolding of Residual Entropy for Speech Codec Learning. https://arxiv.org/abs/2511.22687

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