MIMO Self-Attentive RNN Beamformer for Multi-Speaker Speech Separation
Xiyun Li, Yong Xu, Yu Meng, Shi-Xiong Zhang, Jiaming Xu, Bo Xu, Dong Yu · 2021
Recently, our proposed recurrent neural network (RNN) based all deep learning minimum variance distortionless response (ADL-MVDR) beamformer method yielded superior performance over the conventional MVDR by replacing the matrix inversion and eigenvalue decomposition with two RNNs.In this work, we present a self-attentive RNN beamformer to further improve our previous RNN-based beamformer by leveraging on the powerful modeling capability of self-attention.Temporal-spatial self-attention module is proposed to better learn the beamforming weights from the speech and noise spatial covariance matrices.The temporal self-attention module could help RNN to learn global statistics of covariance matrices.The spatial self-attention module is designed to attend on the cross-channel correlation in the covariance matrices.Furthermore, a multi-channel input with multi-speaker directional features and multi-speaker speech separation outputs (MIMO) model is developed to improve the inference efficiency.The evaluations demonstrate that our proposed MIMO self-attentive RNN beamformer improves both the automatic speech recognition (ASR) accuracy and the perceptual estimation of speech quality (PESQ) against prior arts.