Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks

Hakan Erdogan, John R. Hershey, Shinji Watanabe, Jonathan Le Roux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

220 Citations (Scopus)

Abstract

Separation of speech embedded in non-stationary interference is a challenging problem that has recently seen dramatic improvements using deep network-based methods. Previous work has shown that estimating a masking function to be applied to the noisy spectrum is a viable approach that can be improved by using a signal-approximation based objective function. Better modeling of dynamics through deep recurrent networks has also been shown to improve performance. Here we pursue both of these directions. We develop a phase-sensitive objective function based on the signal-to-noise ratio (SNR) of the reconstructed signal, and show that in experiments it yields uniformly better results in terms of signal-to-distortion ratio (SDR). We also investigate improvements to the modeling of dynamics, using bidirectional recurrent networks, as well as by incorporating speech recognition outputs in the form of alignment vectors concatenated with the spectral input features. Both methods yield further improvements, pointing to tighter integration of recognition with separation as a promising future direction.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages708-712
Number of pages5
Volume2015-August
ISBN (Electronic)9781467369978
DOIs
Publication statusPublished - 2015 Aug 4
Externally publishedYes
Event40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Brisbane, Australia
Duration: 2014 Apr 192014 Apr 24

Other

Other40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015
CountryAustralia
CityBrisbane
Period14/4/1914/4/24

Keywords

  • ASR
  • deep networks
  • LSTM
  • speech enhancement
  • speech separation

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks'. Together they form a unique fingerprint.

  • Cite this

    Erdogan, H., Hershey, J. R., Watanabe, S., & Le Roux, J. (2015). Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks. In 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015 - Proceedings (Vol. 2015-August, pp. 708-712). [7178061] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2015.7178061