Restorative Speech Enhancement: A Progressive Approach Using SE And Codec Modules
2024 · Hsin-Tien Chiang, Hao Zhang, Yong Xu, et al.
Abstract
In challenging environments with significant noise and reverberation, traditional speech enhancement (SE) methods often lead to over-suppressed speech, creating artifacts during listening and harming downstream tasks performance. To overcome these limitations, we propose a novel approach called Restorative SE (RestSE), which combines a lightweight SE module with a generative codec module to progressively enhance and restore speech quality. The SE module initially reduces noise, while the codec module subsequently performs dereverberation and restores speech using generative capabilities. We systematically explore various quantization techniques within the codec module to optimize performance. Additionally, we introduce a weighted loss function and feature fusion that merges the SE output with the original mixture, particularly at segments where the SE output is heavily distorted. Experimental results demonstrate the effectiveness of our proposed method in enhancing speech quality under
Authors
(none)
Tags
Stats
Related papers
- Time-domain Speech Enhancement Assisted By Multi-resolution Frequency Encoder And Decoder (2023)9.76
- Snr-progressive Model With Harmonic Compensation For Low-snr Speech Enhancement (2024)4.52
- Stack Less, Repeat More: A Block Reusing Approach For Progressive Speech Enhancement (2025)0.00
- Sense: Semantic-aware High-fidelity Universal Speech Enhancement (2025)3.85
- Rethinking Processing Distortions: Disentangling The Impact Of Speech Enhancement Errors On Speech Recognition Performance (2024)8.35
- Lisennet: Lightweight Sub-band And Dual-path Modeling For Real-time Speech Enhancement (2024)9.03
- Joint Training Of Speech Enhancement And Self-supervised Model For Noise-robust ASR (2022)0.00
- Bridging The Gap: Integrating Pre-trained Speech Enhancement And Recognition Models For Robust Speech Recognition (2024)7.50