Enhancing speech signal quality in adverse acoustic environments is a persistent challenge in speech processing. Existing deep learning based enhancement methods often struggle to effectively remove background noise and reverberation in real-world scenarios, hampering listening experiences. To address these challenges, we propose a novel approach that uses pre-trained generative methods to resynthesize clean, anechoic speech from degraded inputs. This study leverages pre-trained vocoder or codec models to synthesize high-quality speech while enhancing robustness in challenging scenarios. Generative methods effectively handle information loss in speech signals, resulting in regenerated speech that has improved fidelity and reduced artifacts. By harnessing the capabilities of pre-trained models, we achieve faithful reproduction of the original speech in adverse conditions. Experimental evaluations on both simulated datasets and realistic samples demonstrate the effectiveness and robustness of our proposed methods. Especially by leveraging codec, we achieve superior subjective scores for both simulated and realistic recordings. The generated speech exhibits enhanced audio quality, reduced background noise, and reverberation. Our findings highlight the potential of pre-trained generative techniques in speech processing, particularly in scenarios where traditional methods falter. Demos are available at https://whmrtm.github.io/SoundResynthesis.

翻译：在恶劣声学环境中提升语音信号质量是语音处理领域的一项持续性挑战。现有基于深度学习的增强方法难以有效消除真实场景中的背景噪声与混响，严重制约听觉体验。为解决上述问题，我们提出一种创新方法，利用预训练生成模型从退化输入中重构纯净无混响语音。本研究借助预训练声码器或编解码模型，在提升复杂场景鲁棒性的同时合成高质量语音。生成方法可有效处理语音信号中的信息损失，使重构语音兼具更高保真度与更少伪影。通过挖掘预训练模型潜力，我们实现了恶劣条件下原始语音的高保真重建。基于仿真数据集与真实样本的实验评估表明，所提方法兼具有效性与鲁棒性。特别地，采用编解码模型在仿真与真实录音中均取得了更优的主观评分。生成的语音展现出更优的音频质量、更低的背景噪声与混响。本研究揭示了预训练生成技术在语音处理领域的应用潜力，尤其是在传统方法效果欠佳的场景中。演示音频详见 https://whmrtm.github.io/SoundResynthesis。