An End-to-End Language-Tracking Speech Recognizer for Mixed-Language Speech

Hiroshi Seki, Shinji Watanabe, Takaaki Hori, Jonathan Le Roux, John R. Hershey

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

11 Citations (Scopus)

Abstract

End-to-end automatic speech recognition (ASR) can significantly reduce the burden of developing ASR systems for new languages, by eliminating the need for linguistic information such as pronunciation dictionaries. This also creates an opportunity to build a monolithic multilingual ASR system with a language-independent neural network architecture. In our previous work, we proposed a monolithic neural network architecture that can recognize multiple languages, and showed its effectiveness compared with conventional language-dependent models. However, the model is not guaranteed to properly handle switches in language within an utterance, thus lacking the flexibility to recognize mixed-language speech such as code-switching. In this paper, we extend our model to enable dynamic tracking of the language within an utterance, and propose a training procedure that takes advantage of a newly created mixed-language speech corpus. Experimental results show that the extended model outperforms both language-dependent models and our previous model without suffering from performance degradation that could be associated with language switching.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4919-4923
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 2018 Sep 10
Externally publishedYes
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 2018 Apr 152018 Apr 20

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period18/4/1518/4/20

Keywords

  • End-to-end ASR
  • Hybrid attention/CTC
  • Language identification
  • Language-independent architecture
  • Multilingual ASR

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Seki, H., Watanabe, S., Hori, T., Roux, J. L., & Hershey, J. R. (2018). An End-to-End Language-Tracking Speech Recognizer for Mixed-Language Speech. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 4919-4923). [8462180] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462180