用于目标语音分离的广义时空RNN波束形成器
Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation
摘要：
传统的基于掩码的最小方差无失真响应（MVDR）方法虽然可以减小非线性失真，但MVDR分离语音的残余噪声水平仍然很高。在本文中，我们提出了一个时空递归神经网络波束形成器（RNN-BF）的目标语音分离。这种新的波束形成框架直接从估计的语音和噪声空间协方差矩阵学习波束形成权重。利用RNN的时间建模能力，RNN-BF可以自动累积语音和噪声协方差矩阵的统计信息，以递归方式学习帧级波束成形权重。提出了一种基于RNN的广义特征值波束形成器（RNN-GEV）和一种更广义的RNN波束形成器（GRNN-BF）。我们通过使用层归一化来代替协方差矩阵上常用的掩码归一化，进一步改进了RNN-GEV和GRNN-BF。所提出的GRNN-BF在语音质量（PESQ）、语音噪声比（SNR）和字出错率（WER）方面获得了优于现有技术的性能。
英文摘要：
Although the conventional mask-based minimum variance distortionless response (MVDR) could reduce the non-linear distortion, the residual noise level of the MVDR separated speech is still high. In this paper, we propose a spatio-temporal recurrent neural network based beamformer (RNN-BF) for target speech separation. This new beamforming framework directly learns the beamforming weights from the estimated speech and noise spatial covariance matrices. Leveraging on the temporal modeling capability of RNNs, the RNN-BF could automatically accumulate the statistics of the speech and noise covariance matrices to learn the frame-level beamforming weights in a recursive way. An RNN-based generalized eigenvalue (RNN-GEV) beamformer and a more generalized RNN beamformer (GRNN-BF) are proposed. We further improve the RNN-GEV and the GRNN-BF by using layer normalization to replace the commonly used mask normalization on the covariance matrices. The proposed GRNN-BF obtains better performance against prior arts in terms of speech quality (PESQ), speech-to-noise ratio (SNR) and word error rate (WER).
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