Multichannel Variational Autoencoder-Based Speech Separation in Designated Speaker Order

Abstract

The multichannel variational autoencoder (MVAE) integrates the rule-based update of a separation matrix and the deep generative model and proves to be a competitive speech separation method. However, the output (global) permutation ambiguity still exists and turns out to be a fundamental problem in applications. In this paper, we address this problem by employing two dedicated encoders. One encodes the speaker identity for the guidance of the output sorting, and the other encodes the linguistic information for the reconstruction of the source signals. The instance normalization (IN) and the adaptive instance normalization (adaIN) are applied to the networks to disentangle the speaker representations from the content representations. The separated sources are arranged in designated order by a symmetric permutation alignment scheme. In the experiments, we test the proposed method in different gender combinations and various reverberant conditions and generalize it to unseen speakers. The results validate its reliable sorting accuracy and good separation performance. The proposed method outperforms the other baseline methods and maintains stable performance, achieving over 20 dB SIR improvement even in high reverberant environments.