Student-Teacher Learning for BLSTM Mask-based Speech Enhancement

Abstract

Spectral mask estimation using bidirectional long short-term memory (BLSTM) neural networks has been widely used in various speech enhancement applications, and it has achieved great success when it is applied to multichannel enhancement techniques with a mask-based beamformer.However, when these masks are used for single channel speech enhancement they severely distort the speech signal and make them unsuitable for speech recognition.This paper proposes a studentteacher learning paradigm for single channel speech enhancement.The beamformed signal from multichannel enhancement is given as input to the teacher network to obtain soft masks.An additional cross-entropy loss term with the soft mask target is combined with the original loss, so that the student network with single-channel input is trained to mimic the soft mask obtained with multichannel input through beamforming.Experiments with the CHiME-4 challenge single channel track data shows improvement in ASR performance.