Language Identification Using Pitch Contour Information
Chi-Yueh Lin, Hsiao-Chuan Wang · 2006
An approach to automatic language identification (LID) using pitch contour information is proposed. A segment of pitch contour is approximated by a set of Legendre polynomials so that coefficients of the polynomials form a feature vector to represent this pitch contour. A Gaussian mixture model (GMM) method based on feature vectors extracted from pitch contours is suggested for the LID. Our experiments show that only two or three coefficients are necessary to obtain reasonably good identification rates. We also find that the length of the segmented pitch contour is another useful feature for LID, so that it is included to improve the performance further. Pair-wise language identification experiments on the OGI-TS corpus show that our proposed approach is very promising We also find that tonal languages and pitch accent languages achieve better performance in our system.