Inspired by recent developments in neural speech coding and diffusion-based language modeling, we tackle speech enhancement by modeling the conditional distribution of clean speech codes given noisy speech codes using absorbing discrete diffusion. The proposed approach, which we call ADDSE, leverages both the expressive latent space of neural audio codecs and the non-autoregressive sampling procedure of diffusion models. To efficiently model the hierarchical structure of residual vector quantization codes, we propose RQDiT, which combines techniques from RQ-Transformer and diffusion Transformers for non-autoregressive modeling. Results show competitive performance in terms of non-intrusive objective metrics on two datasets, especially at low signal-to-noise ratios and with few sampling steps. Code and audio examples are available online.

翻译：受神经语音编码和基于扩散的语言建模最新进展的启发，我们通过吸收态离散扩散建模含噪语音编码条件下干净语音编码的条件分布，来解决语音增强问题。所提出的方法（称为ADDSE）同时利用了神经音频编解码器的高表达潜空间和扩散模型的非自回归采样过程。为高效建模残差向量量化编码的层次化结构，我们提出RQDiT——该模型融合了RQ-Transformer与扩散Transformer的技术，实现非自回归建模。结果表明，在两个数据集上的非侵入式客观指标中，该方法在低信噪比和少采样步数场景下均展现出具有竞争力的性能。相关代码和音频示例已在线公开。