Name
Abstract | ||
|---|---|---|
Speech enhancement is an essential task of improving speech quality in noise scenario. Several state-of-the-art approaches have introduced visual information for speech enhancement,since the visual aspect of speech is essentially unaffected by acoustic environment. This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN). In par-ticular, the proposed visual speech enhancement GAN consistof two networks trained in adversarial manner, i) a generator that adopts multi-layer feature fusion convolution network to enhance input noisy speech, and ii) a discriminator that attemptsto minimize the discrepancy between the distributions of the clean speech signal and enhanced speech signal. Experiment re-sults demonstrated superior performance of the proposed modelagainst several state-of-the-art |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICASSP43922.2022.9747187 | IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinmeng Xu | 1 | 0 | 1.35 |
| Y.-Y. Wang | 2 | 539 | 75.11 |
| Dongxiang Xu | 3 | 0 | 0.68 |
| Yiyuan Peng | 4 | 0 | 1.01 |
| Cong Zhang | 5 | 0 | 2.37 |
| Jie Jia | 6 | 0 | 1.35 |
| Binbin Chen | 7 | 240 | 31.18 |