标题： ParaGSE：基于组向量量化神经语音编解码器的并行生成语音增强 显示英文标题

标题： ParaGSE: Parallel Generative Speech Enhancement with Group-Vector-Quantization-based Neural Speech Codec

摘要： 最近，生成式语音增强引起了广泛关注；然而，现有的方法受到过度复杂性、有限效率和次优语音质量的限制。 为了克服这些挑战，本文提出了一种新颖的并行生成式语音增强（ParaGSE）框架，该框架利用基于组向量量化（GVQ）的神经语音编解码器。 基于GVQ的编解码器采用独立的VQ来生成相互独立的标记，从而在ParaGSE中实现高效的并行标记预测。 具体来说，ParaGSE利用基于GVQ的编解码器将退化的语音编码为不同的标记，通过并行分支根据退化频谱特征预测相应的干净标记，并最终通过编解码器解码器重建干净语音。 实验结果表明，在包括噪声、混响、带宽限制及其混合在内的广泛失真情况下，ParaGSE始终能产生优于判别式和生成式基线的增强语音。 此外，得益于标记预测中的并行计算，与串行生成式语音增强方法相比，ParaGSE在CPU上的生成效率提高了约1.5倍。 显示英文摘要

摘要： Recently, generative speech enhancement has garnered considerable interest; however, existing approaches are hindered by excessive complexity, limited efficiency, and suboptimal speech quality. To overcome these challenges, this paper proposes a novel parallel generative speech enhancement (ParaGSE) framework that leverages a group vector quantization (GVQ)-based neural speech codec. The GVQ-based codec adopts separate VQs to produce mutually independent tokens, enabling efficient parallel token prediction in ParaGSE. Specifically, ParaGSE leverages the GVQ-based codec to encode degraded speech into distinct tokens, predicts the corresponding clean tokens through parallel branches conditioned on degraded spectral features, and ultimately reconstructs clean speech via the codec decoder. Experimental results demonstrate that ParaGSE consistently produces superior enhanced speech compared to both discriminative and generative baselines, under a wide range of distortions including noise, reverberation, band-limiting, and their mixtures. Furthermore, empowered by parallel computation in token prediction, ParaGSE attains about a 1.5-fold improvement in generation efficiency on CPU compared with serial generative speech enhancement approaches.