Comparing different approaches for automatic pronunciation error detection

Helmer Strik, Khiet Phuong Truong, Febe de Wet, Catia Cucchiarini

    Research output: Contribution to journalArticleAcademic

    105 Citations (Scopus)
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    Abstract

    One of the biggest challenges in designing computer assisted language learning (CALL) applications that provide automatic feedback on pronunciation errors consists in reliably detecting the pronunciation errors at such a detailed level that the information provided can be useful to learners. In our research we investigate pronunciation errors frequently made by foreigners learning Dutch as a second language. In the present paper we focus on the velar fricative /x/ and the velar plosive /k/. We compare four types of classifiers that can be used to detect erroneous pronunciations of these phones: two acoustic–phonetic classifiers (one of which employs Linear Discriminant Analysis (LDA)), a classifier based on cepstral coefficients in combination with LDA, and one based on confidence measures (the so-called Goodness Of Pronunciation score). The best results were obtained for the two LDA classifiers which produced accuracy levels of about 85–93%.
    Original languageUndefined
    Pages (from-to)845-852
    JournalSpeech communication
    Volume51
    Issue number10
    DOIs
    Publication statusPublished - 2009

    Keywords

    • IR-79841
    • Acoustic–phonetic classification
    • Computer assisted language learning
    • Computer assisted pronunciation training
    • Pronunciation error detection

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