8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Comparing Classifiers for Pronunciation Error Detection

Helmer Strik (1), Khiet P. Truong (2), Febe de Wet (3), Catia Cucchiarini (1)

(1) Radboud University Nijmegen, The Netherlands
(2) TNO Human Factors, The Netherlands
(3) Stellenbosch University, South Africa

Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: 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 scores). The best results were obtained for the two LDA classifiers which produced accuracy levels of about 85-93%.

Full Paper

Bibliographic reference.  Strik, Helmer / Truong, Khiet P. / Wet, Febe de / Cucchiarini, Catia (2007): "Comparing classifiers for pronunciation error detection", In INTERSPEECH-2007, 1837-1840.