Student-teacher learning for BLSTM mask-based speech enhancement

Aswin Shanmugam Subramanian, Szu Jui Chen, Shinji Watanabe

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2018 Mar 27
Externally publishedYes


  • Mask estimation
  • Speech enhancement
  • Speech recognition
  • Student-teacher learning

ASJC Scopus subject areas

  • General

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