# Student-Teacher Learning for BLSTM Mask-based Speech Enhancement

arXiv: https://arxiv.org/abs/1803.10013 · HTML: https://mlsanity.com/paper/1803.10013/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [A Study on Speech Enhancement Based on Diffusion Probabilistic Model](https://mlsanity.com/paper/2107.11876/) (2021)
- [Ultra Fast Speech Separation Model with Teacher Student Learning](https://mlsanity.com/paper/2204.12777/) (2022)
- [Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization](https://mlsanity.com/paper/2210.03459/) (2022)

## Abstract (arXiv)

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 student-teacher 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.