Some acoustic improvements for pronunciation quality assessment for strongly accented mandarin speech

This paper presents our recent study in resolving some specific acoustic problems of the computer assisted language learning (CALL) system by modifying the acoustic model (AM) and feature under ASR framework. Firstly, speaker dependent cepstrum mean normalization (Speaker CMN) is adopted to alleviate the distortion of channel, with which the average human-machine scoring correlation coefficient (ACC) is improved from 78.00% to 84.14%. Heteroscedastic linear discriminate analysis (HLDA) is then applied to enhance the discrimination ability of AM, which successfully increases ACC from 84.14% to 84.62%. Additionally, HLDA can lessen the great human-machine scoring difference of those speeches that have very good or too bad pronunciation quality, and so lead to an increase of the correctly-rank rate (CRR) from 85.59% to 90.99%. Finally, we use maximum a posteriori (MAP) to tune AM to match the strong accented test speech. As the result, ACC is improved from 84.62% to 86.57%.