用预训练语音增强模型分阶段量化，提升低码率语音编码稳定性与质量。
PURE Codec: Progressive Unfolding of Residual Entropy for Speech Codec Learning
- 分阶段量化：先处理低熵去噪特征，再编码高熵残差
- 在噪声训练下仍优于传统方法，重建质量与下游任务表现更优
- 适合追求高稳定性和高质量语音压缩的研究者
神经语音编码器在低码率压缩中表现优异，但残差向量量化（RVQ）常因训练不稳定和分解无效而影响重建质量与效率。本文提出PURE Codec（Progressive Unfolding of Residual Entropy），利用预训练语音增强模型引导多阶段量化：首阶段重建低熵、去噪的语音嵌入，后续阶段编码高熵残差成分。该设计显著提升训练稳定性。实验表明，PURE在重建质量及基于语音语言模型的文语转换任务中持续优于传统RVQ编码器，尤其在噪声训练条件下表现更佳。
原文摘要 · Abstract (English)
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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