Recognizing zero-resourced languages based on mismatched machine transcriptions

Wenda Chen, Mark Hasegawa-Johnson, Nancy F. Chen

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

Abstract

Mismatched crowdsourcing based probabilistic human transcription has been proposed recently for training and adapting acoustic models for zero-resourced languages where we do not have any native transcriptions. This paper describes a machine transcription based phone recognition system for recognizing zero-resourced languages and compares it with baseline systems of MAP adaptation and semi-supervised self training. With a set of available speech recognizers in source languages that cover all the basic phonetic features, this work shows that we can use mismatched machine transcriptions from these source languages to achieve human level transcriptions, bypassing the laborious efforts of obtaining human transcriptions. We also present a fully automated unsupervised approach for zero-resourced speech recognition using mismatched machine transcriptions for transfer learning of phone models.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5979-5983
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Keywords

  • Automatic speech recognition (ASR)
  • Mismatched machine transcription
  • Modular system
  • Transfer learning
  • Zero-resourced languages

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Chen, W., Hasegawa-Johnson, M., & Chen, N. F. (2018). Recognizing zero-resourced languages based on mismatched machine transcriptions. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 5979-5983). [8462481] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462481