Chinese intonation assessment using SEV features

Intonation assessment is an important part of Chinese CALL system. Nowadays, most systems use the correlation and RMSE features to assess the quality of the intonation of a given speech. As correlation and RMSE assign unoptimized weights to different degrees of mismatching errors, they may lead to performance degradation. In this paper, we propose a new feature called sorted error vector (SEV) for intonation assessment. The basic idea is to calculate mismatching quantities, sort them with ascending order, and then re-sample them to a K-points vector. This feature has four benefits: first, it is text-length independent; second, weights are let to train by classifiers; third, the relationship between the errors and the final results is not limited to any assumption; fourth, SEV is not sensitive to the performance of different pitch extracting algorithms. Experiments show that no matter in which case, SEV feature performs the best.