Accent neutralization for speech recognition of non-native speakers

Kacper Radzikowski, Mateusz Forc, Le Wang, Osamu Yoshie, Robert Nowak

研究成果: Conference contribution

抄録

These days, automatic speech recognition (ASR) systems achieve higher and higher accuracy rates. The score drops significantly, in case when the ASR system is being used with a non-native speaker of the language to be recognized. The main reason is specific pronunciation and accent features. A limited volume of labeled nonnative speech datasets makes it difficult to train new ASR systems for non-native speakers. In our research,we tried tackling the problem and its influence on the accuracy of ASR systems, using the style transfer methodology. We designed a pipeline for modifying the speech of a non-native speaker, so that it resembles the native speech to a higher extent. Our methodology can be used as a wrapper for any existing ASR system, which reduces the necessity of training new algorithms for non-native speech. The modification can be thus performed before passing the data forward to the speech recognition system itself.

本文言語English
ホスト出版物のタイトル21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings
編集者Maria Indrawan-Santiago, Eric Pardede, Ivan Luiz Salvadori, Matthias Steinbauer, Ismail Khalil, Gabriele Anderst-Kotsis
出版社Association for Computing Machinery
ISBN(電子版)9781450371797
DOI
出版ステータスPublished - 2019 12 2
イベント21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Munich, Germany
継続期間: 2019 12 22019 12 4

出版物シリーズ

名前ACM International Conference Proceeding Series

Conference

Conference21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019
CountryGermany
CityMunich
Period19/12/219/12/4

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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