한국인의 외국어 발화오류검출 음성인식기에서 청취판단과 상관관계가 높은 기계 스코어링 기법

Machine Scoring Methods Highly-correlated with Human Ratings in Speech Recognizer Detecting Mispronunciation of Foreign Language

An automatic pronunciation correction system provides users with correction guidelines for each pronunciation error. For this purpose, we develop a speech recognition system which automatically classifies pronunciation errors when Koreans speak a foreign language. In this paper, we propose a machine scoring method for automatic assessment of pronunciation quality by the speech recognizer. Scores obtained from an expert human listener are used as the reference to evaluate the different machine scores and to provide targets when training some of algorithms. We use a log-likelihood score and a normalized log-likelihood score as machine scoring methods. Experimental results show that the normalized log-likelihood score had higher correlation with human scores than that obtained using the log-likelihood score.