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The end-to-end approaches for single-channel target speech extraction haveattracted widespread attention. However, the studies for end-to-endmulti-channel target speech extraction are still relatively limited. In thiswork, we propose two methods for exploiting the multi-channel spatialinformation to extract the target speech. The first one is using a targetspeech adaptation layer in a parallel encoder architecture. The second one isdesigning a channel decorrelation mechanism to extract the inter-channeldifferential 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 datasetdemonstrate that our proposed methods achieve up to 11.2% and 11.5% relativeimprovements in SDR and SiSDR respectively, which are the best reported resultson this task to the best of our knowledge.

Multi-channel target speech extraction with channel decorrelation and target speaker adaptation