Accent neutralization for speech recognition of non-native speakers

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

研究成果

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ICST
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PervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN(印刷版)2153-1633

Conference

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

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • コンピュータ サイエンスの応用
  • 健康情報学

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