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Multi-Channel Target Speech Extraction with Channel Decorrelation and Target Speaker Adaptation

Citation Author(s):
Jiangyu Han, Xinyuan Zhou, Yanhua Long , Yijie Li
Submitted by:
jy han
Last updated:
22 June 2021 - 4:41am
Document Type:
Poster
Document Year:
2021
Event:
Presenters:
Jiangyu Han
Paper Code:
1448
 

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.

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