Prosodic Features Based Text-dependent Speaker Recognition with Short Utterance

Abstract

Over the past several years, Gaussian mixtures models have been the dominant approach for modeling in text-independent speaker recognition field. But the recognition accuracy for these models declines when utterances’ length becomes short. Presently Mel-frequency cepstral coefficients are generally used to characterize the properties of the vocal tract and widely applied in speech recognition. In addition, prosodic features, such as pitch and formant, are generally considered to describe the glottal characteristics. However, the efficiency of those approaches remain unsatisfactory. In text-dependent short utterances speaker verification systems, prosodic features can assist to improve the recognition result theoretically. In order to optimize the performance of speaker verification systems under the framework of adapted GMM-UBM, we adopt a variant speaker verification system based on prosodic features, in which a dual-judgment-mechanism is used in order to integrate vocal tract features with prosodic features. Experimental results showed that the new speech recognition system led a better consequence.