We have previously proposed a statistical method for estimating the pronunciation proficiency and intelligibility of presentations made in English by non-native speakers. To investigate the relationship between various acoustic measures and the pronunciation score and intelligibility, we statistically analyzed the speaker’s actual utterances to find combinations of acoustic features with a high correlation between the score estimated by a linear regression model and the score perceived by native English teachers. In this paper, we examined the quality of new acoustic features that are useful when used in combination with the system’s estimates of pronunciation score and intelligibility. Results showed that the best combination of acoustic features produced correlation coefficients of 0.929 and 0.753 for pronunciation and intelligibility, respectively, using open data for speakers at the 10-sentence level. Index Terms: pronunciation evaluation, intelligibility evaluation, English, HMM, phoneme pair, perplexity
['Hiroshi Kibishi', 'Seiichi Nakagawa']
New feature parameters for pronunciation evaluation in English presentations at international conferences