  Recently, deep learning-based beamforming algorithms have shown promising
performance in target speech extraction tasks. However, most systems do not
fully utilize spatial information. In this paper, we propose a target speech
extraction network that utilizes spatial information to enhance the performance
of neural beamformer. To achieve this, we first use the UNet-TCN structure to
model input features and improve the estimation accuracy of the speech
pre-separation module by avoiding information loss caused by direct
dimensionality reduction in other models. Furthermore, we introduce a
multi-head cross-attention mechanism that enhances the neural beamformer's
perception of spatial information by making full use of the spatial information
received by the array. Experimental results demonstrate that our approach,
which incorporates a more reasonable target mask estimation network and a
spatial information-based cross-attention mechanism into the neural beamformer,
effectively improves speech separation performance.
