Efficient feature extraction of speaker identification using phoneme mean F-ratio for Chinese

Abstract

The features used for speaker recognition should have more speaker individual information while attenuating the linguistic information. In order to discard the linguistic information effectively, in this paper, we employed the phoneme mean F-ratio method to investigate the different contributions of different frequency region from the point of view of Chinese phoneme, and apply it for speaker identification. It is found that the speaker individual information depending on the phonemes is distributed in different frequency regions of speech sound. Based on the contribution rate, we extracted the new features and combined with GMM model. The experiment for speaker identification task is conducted with a King-ASR Chinese database. Compared with the MFCC feature, the identification error rate with the proposed feature was reduced by 32.94%. The results confirmed that the efficiency of the phoneme mean F-ratio method for improving speaker recognition performance for Chinese.