Multi-channel target speech extraction with channel decorrelation and target speaker adaptation
| Autor: | Yijie Li, Yanhua Long, Xinyuan Zhou, Jiangyu Han |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science Audio and Speech Processing (eess.AS) Speech recognition Feature extraction FOS: Electrical engineering electronic engineering information engineering Representation (mathematics) Adaptation (computer science) Encoder Spatial analysis Decorrelation Communication channel Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | ICASSP |
| DOI: | 10.48550/arxiv.2010.09191 |
| Popis: | The end-to-end approaches for single-channel target speech extraction have attracted widespread attention. However, the studies for end-to-end multi-channel target speech extraction are still relatively limited. In this work, we propose two methods for exploiting the multi-channel spatial information to extract the target speech. The first one is using a target speech adaptation layer in a parallel encoder architecture. The second one is designing a channel decorrelation mechanism to extract the inter-channel differential information to enhance the multi-channel encoder representation. We compare the proposed methods with two strong state-of-the-art baselines. Experimental results on the multi-channel reverberant WSJ0 2-mix dataset demonstrate that our proposed methods achieve up to 11.2% and 11.5% relative improvements in SDR and SiSDR respectively, which are the best reported results on this task to the best of our knowledge. Comment: 5 pages, 3 figures. Submitted to ICASSP 2021 |
| Databáze: | OpenAIRE |
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