Acoustic models for pronunciation assessment of vowels of Indian English

Abstract

We consider the pronunciation assessment of vowels of Indian English uttered by speakers with Gujarati L1 using confidence measures obtained by automatic speech recognition. The goodness-of-pronunciation measure as captured by the acoustic likelihood scores can be effective only when the acoustic models used are appropriate for the task i.e. detecting errors in the target language (Indian English) typical of speakers of Gujarati (the source language). Thus the speech data used for acoustic model training is expected to have a prominent influence on system performance. In the absence of labeled speech databases of either the source or target language, we investigate specific combinations of acoustic models trained on available databases of American English and Hindi. It is observed that Indian English speech is better represented by Hindi speech models for vowels common to the two languages rather than by American English models. Further, adaptation with a limited amount of Indian English speech improves the system performance.