Comparing different approaches for automatic pronunciation error detection

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%.