Single-channel multi-speaker separation using deep clustering

Yusuf Isik, Jonathan Le Roux, Zhuo Chen, Shinji Watanabe, John R. Hershey

Research output: Contribution to journalArticle

83 Citations (Scopus)

Abstract

Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation. We first significantly improve upon the baseline system performance by incorporating better regularization, larger temporal context, and a deeper architecture, culminating in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1 dB SDR improvement for three-speaker separation. We then extend the model to incorporate an enhancement layer to refine the signal estimates, and perform end-to-end training through both the clustering and enhancement stages to maximize signal fidelity. We evaluate the results using automatic speech recognition. The new signal approximation objective, combined with end-to-end training, produces unprecedented performance, reducing the word error rate (WER) from 89.1% down to 30.8%. This represents a major advancement towards solving the cocktail party problem.

Original languageEnglish
Pages (from-to)545-549
Number of pages5
JournalUnknown Journal
Volume08-12-September-2016
DOIs
Publication statusPublished - 2016
Externally publishedYes

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Keywords

  • Deep learning
  • Embedding
  • Single-channel speech separation

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

Cite this

Isik, Y., Le Roux, J., Chen, Z., Watanabe, S., & Hershey, J. R. (2016). Single-channel multi-speaker separation using deep clustering. Unknown Journal, 08-12-September-2016, 545-549. https://doi.org/10.21437/Interspeech.2016-1176