Adaptive and Discriminative Modeling for Improved Mispronunciation Detection

HORACIO FRANCO; LUCIANA FERRER; HARRY BRATT

Stockholm

Congreso; IS ADEPT; 2012

In the context of computer-aided language learning, automatic detection of specific phone mispronunciations by nonnative speakers can be used to provide detailed feedback for a student to work with specific pronunciation problems when producing the new sounds of a foreign language. Starting with an initial approach based on a measure of match to native models, we found that significant improvements could be achieved by explicitly modeling both mispronunciations and correct pronunciations by nonnative speakers. In this work, our approach is extended based on the use of adaptation and discriminative modeling, showing significant improvements from our previous best system. Performance of the proposed approaches was evaluated in a phonetically transcribed database of 130,000 phones uttered in continuous speech sentences by 206 nonnative speakers.