An Effective CALL System for Strongly Accented Mandarin Speech

In this paper, we investigate some specific acoustic problems of the computer assisted language learning (CALL) system by modifying the acoustic model and feature under the speech recognition framework. At first, in order to alleviate the distortion of channel and speaker, speaker-dependent Cepstrum Mean Normalization (Speaker CMN) is adopted, by which the average correlation coefficient (ACC) between human and machine scores is improved from 78.00% to 84.14%. Then, Heteroscedastic Linear Discriminate Analysis (HLDA) is applied to enhance the discrimination ability of acoustic model, which successfully increases ACC from 84.14% to 84.62%. Additionally, HLDA can lessen the great human-machine scoring difference of speeches that have very good or too bad quality, and so leads to an increase of the correctly-rank rate from 85.59% to 90.99%. Finally, we use the technology of Maximum a Posteriori (MAP) to tune the acoustic model to match the strongly accented testing speech. As the result, ACC is improved from 84.62% to 86.57%.