An investigation of dependencies between frequency components and speaker characteristics for text-independent speaker identification

The features used for speech recognition are expected to emphasize linguistic information while suppressing individual differences. For speaker recognition, in contrast, features should preserve individual information and attenuate the linguistic information at the same time. In most studies, however, identical acoustic features are used for the different missions of speaker and speech recognition. In this paper, we first investigated the relationships between the frequency components and the vocal tract based on speech production. We found that the individual information is encoded non-uniformly in different frequency bands of speech sound. Then we adopted statistical Fisher’s F-ratio and information-theoretic mutual information measurements to measure the dependencies between frequency components and individual characteristics based on a speaker recognition database (NTT-VR). From the analysis, we not only confirmed the finding of non-uniform distribution of individual information in different frequency bands from the speech production point of view, but also quantified their dependencies. Based on the quantification results, we proposed a new physiological feature which emphasizes individual information for text-independent speaker identification by using a non-uniform subband processing strategy to emphasize the physiological information involved in speech production. The new feature was combined with GMM speaker models and applied to the NTT-VR speaker recognition database. The speaker identification using proposed feature reduced the identification error rate 20.1% compared that with MFCC feature. The experimental results confirmed that emphasizing the features from highly individual-dependent frequency bands is valid for improving speaker recognition performance.