Landmark-based automated pronunciation error detection

Su Youn Yoon, Mark Allan Hasegawa-Johnson, Richard Sproat

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

Abstract

We present a pronunciation error detection method for second language learners of English (L2 learners). The method is a combination of confidence scoring at the phone level and landmark-based Support Vector Machines (SVMs). Landmark-based SVMs were implemented to focus the method on targeting specific phonemes in which L2 learners make frequent errors. The method was trained on the phonemes that are difficult for Korean learners and tested on intermediate Korean learners. In the data where non-phonemic errors occurred in a high proportion, the SVM method achieved a significantly higher F-score (0.67) than confidence scoring (0.60). However, the combination of the two methods without the appropriate training data did not lead to improvement. Even for intermediate learners, a high proportion of errors (40%) was related to these difficult phonemes. Therefore, a method that is specialized for these phonemes would be beneficial for both beginners and intermediate learners.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
Pages614-617
Number of pages4
StatePublished - 2010
Event11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010 - Makuhari, Chiba, Japan
Duration: Sep 26 2010Sep 30 2010

Other

Other11th Annual Conference of the International Speech Communication Association: Spoken Language Processing for All, INTERSPEECH 2010
CountryJapan
CityMakuhari, Chiba
Period9/26/109/30/10

Keywords

  • Automated pronunciation error detection
  • Computer-aided pronunciation training systems
  • Landmark-based SVMs
  • Phone-level confidence scores

ASJC Scopus subject areas

  • Language and Linguistics
  • Speech and Hearing

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