Automatic evaluation of English pronunciation by Japanese speakers using various acoustic features and pattern recognition techniques
Kuniaki Hirabayashi, Seiichi Nakagawa · 2010
In this paper, we propose a method for estimating a score for English pronunciation. Scores estimated by the proposed method were evaluated by correlating them with the learner’s pronunciation score which was scored by native English teachers. The average correlation between the estimated pronunciation scores and the learner’s pronunciation scores over 1, 5, and 10 sentences was 0.807, 0.873, and 0.921, respectively. When a text of spoken sentence was unknown, we obtained a correlation of 0.878 for 10 utterances. For English phonetic evaluation, we classified English phoneme pairs that are difficult for Japanese speakers to pronounce, using SVM, NN, and HMM classifiers. The correct classification ratios for native English and Japanese English phonemes were 94.6% and 92.3% for SVM, 96.5% and 87.4% for NN, 85.0% and 69.2% for HMM, respectively. We then investigated the relationship between the classification rate and a proficiency score of non-native learner’s English pronunciation, and obtained a high correlation of 0.6 ∼ 0.7. Index Terms: pronunciation evaluation, English, Japanese, HMM, SVM, Neural Network