Combining Cepstral and Prosodic Features in Language Identification

A novel approach of combining cepstral features and prosodic features in language identification is presented in this paper. This combination approach shows a significant improvement on a GMM-UBM based language identification (LID) system which utilizes modern shifted delta cepstrum (SDC) and feature warping techniques. The proposed system achieves a high accuracy of 87.1% on a 10-language task, and outperforms the baseline system by 12%. The prosodic features are proven to be very effective in both tonal and non-tonal LID, as they deliver new language-discrimination information in addition to those from widely used cepstral features. Additionally, the performance of MFCC and PLP features with different coefficient numbers in language identification tasks are researched and compared. Less number of coefficients is more likely to be sufficient or even better for language identification