  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.
