Gaussian selection for speaker recognition using cumulative vectors

Abstract

Speaker recognition systems frequently use GMM - MAP method for modeling speakers. This method represents a speaker using a Gaussian mixture. However in this mixture not all the Gaussian components are truly representative of the speaker. In order to remove the model redundancy, this work proposes a Gaussian selection method to achieve a new GMM model only with the more representative Gaussian components. Speaker verification experiments applying the proposal show a similar performance to baseline; however the speaker models have a reduction of 80 % regarding the speaker model used for baseline. The application of this Gaussian selection method in real or embedded speaker verification systems could be very useful for reducing computational and memory cost. © 2012 Springer-Verlag.