SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916796049653760 |
|---|---|
| author | Li, Sirui Wang, Shuai Liu, Zhijun Jiang, Zhongjie Wang, Yannan Li, Haizhou |
| author_facet | Li, Sirui Wang, Shuai Liu, Zhijun Jiang, Zhongjie Wang, Yannan Li, Haizhou |
| contents | Speech pre-processing techniques such as denoising, de-reverberation, and separation, are commonly employed as front-ends for various downstream speech processing tasks. However, these methods can sometimes be inadequate, resulting in residual noise or the introduction of new artifacts. Such deficiencies are typically not captured by metrics like SI-SNR but are noticeable to human listeners. To address this, we introduce SpeechRefiner, a post-processing tool that utilizes Conditional Flow Matching (CFM) to improve the perceptual quality of speech. In this study, we benchmark SpeechRefiner against recent task-specific refinement methods and evaluate its performance within our internal processing pipeline, which integrates multiple front-end algorithms. Experiments show that SpeechRefiner exhibits strong generalization across diverse impairment sources, significantly enhancing speech perceptual quality. Audio demos can be found at https://speechrefiner.github.io/SpeechRefiner/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms Li, Sirui Wang, Shuai Liu, Zhijun Jiang, Zhongjie Wang, Yannan Li, Haizhou Audio and Speech Processing Sound Speech pre-processing techniques such as denoising, de-reverberation, and separation, are commonly employed as front-ends for various downstream speech processing tasks. However, these methods can sometimes be inadequate, resulting in residual noise or the introduction of new artifacts. Such deficiencies are typically not captured by metrics like SI-SNR but are noticeable to human listeners. To address this, we introduce SpeechRefiner, a post-processing tool that utilizes Conditional Flow Matching (CFM) to improve the perceptual quality of speech. In this study, we benchmark SpeechRefiner against recent task-specific refinement methods and evaluate its performance within our internal processing pipeline, which integrates multiple front-end algorithms. Experiments show that SpeechRefiner exhibits strong generalization across diverse impairment sources, significantly enhancing speech perceptual quality. Audio demos can be found at https://speechrefiner.github.io/SpeechRefiner/. |
| title | SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2506.13709 |