Variational recurrent neural networks for speech separation

Jen-Tzung Chien, Kuan Ting Kuo

Research output: Contribution to journalConference article

14 Scopus citations

Abstract

We present a new stochastic learning machine for speech separation based on the variational recurrent neural network (VRNN). This VRNN is constructed from the perspectives of generative stochastic network and variational auto-encoder. The idea is to faithfully characterize the randomness of hidden state of a recurrent neural network through variational learning. The neural parameters under this latent variable model are estimated by maximizing the variational lower bound of log marginal likelihood. An inference network driven by the variational distribution is trained from a set of mixed signals and the associated source targets. A novel supervised VRNN is developed for speech separation. The proposed VRNN provides a stochastic point of view which accommodates the uncertainty in hidden states and facilitates the analysis of model construction. The masking function is further employed in network outputs for speech separation. The benefit of using VRNN is demonstrated by the experiments on monaural speech separation.

Original languageEnglish
Pages (from-to)1193-1197
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2017-August
DOIs
StatePublished - 1 Jan 2017
Event18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017

Keywords

  • Recurrent neural network
  • Speech separation
  • Variational learning

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