Speaker Identification using FM Features

Abstract

The AM-FM modulation model of speech is a nonlinear model that has been successfully used in several branches of speech-related research. However, the significance of the AM-FM features extracted from this model has not been fully explored in applications such as speaker identification systems. This paper shows that frequency modulation (FM) features can improve speaker identification accuracy. Due to the similarity between amplitude modulation (AM) feature and the conventional Mel frequency cepstrum coefficients (MFCC), this paper mainly focuses on the FM feature. The correlation between FM feature components is shown to be very small compared with that of Mel filterbank log energies, thus reducing the need for decorrelation. FM feature components are shown to be very nearly Gaussian distributed. Further, speech synthesis using AM-FM features is performed to compare four existing AM-FM demodulation methods based on the perceptual quality of the synthesized speech. Of these, Digital Energy Separation Algorithm (DESA) gives the best synthesized speech, and is thus used as a front-end in our speaker identification system. Evaluation of speaker identification using FM features on the NIST 2001 database shows a relative improvement in speaker identification accuracy of 2% for male speakers and 9% for female speakers over the conventional MFCC-based frontend.