Using cepstral and prosodic features for Chinese accent identification

In this paper, we propose an approach for Chinese accent identification using both cepstral and prosodic features with gender-dependent model. We exploit a combination of conventional Shifted Delta Cepstrum (SDC) features and pitch contour features as an example of segmental and suprasegmental features, to capture the characteristics in Chinese accents. We use cubic polynomials to estimate the pitch contour segments in order to model the differences within accents. We train gender-dependent GMM acoustic models to express the features in order to deal with the gender variation. Since conventional criterion of the GMM assumption cannot solve those multi-feature problems, we use the support vector machine (SVM) to make the decision. We evaluated the effectiveness of the proposed approach on the 863 Chinese accent database. The result shows that our approach yields a 15.5% relative error rate reduction compared to conventional approaches of using only SDC features.