Automatic detection of voice onset time contrasts for use in pronunciation assessment

Abstract

This study examines methods for recognizing different classes of phones from accented speech based on voice on-set time (VOT). These methods are tested on data from the Tball corpus of Los Angeles-area elementary school chil-dren [1]. The methods proposed and tested are: 1) to train models based on standard English VOT contrasts and then extract the VOT characteristics of the phones by measuring the duration of phone-level and sub-phone-level alignments, 2) to train phone models with explicit aspiration, and 3) to train different models for different phoneme classes of VOT times. Error rates of 23-53 % for different phone classes are reported for the rst method, 5-57 % for the second method, and 0-36 % for the third. The results show that different methods work better on different phone classes. We inter-pret these results in relation to past research on VOT, explain possible uses for these ndings, and propose directions for future research. Index Terms: recognition of speech variation, pronuncia-tion variation, voice onset time.