αα-Gaussian mixture modelling for speaker recognition

Gaussian mixture model is the conventional approach employed in speaker recognition tasks. Although it is efficient to model specific speaking characteristics of a speaker, especially in quiet environments, its performance in noisy conditions is still far from the human cognitive process. Recently, a new method of αα-integration of stochastic models has been proposed based on psychophysical experiments that suggests αα-integration is used in a human brain. In this paper, we proposed a method to extend the conventional GMM to the αα-integrated GMM (αα-GMM) to model personal speaking traits. Model parameters were re-estimated recursively based on a given data set. The experiments showed that the new approach significantly outperforms the traditional method, especially on telephony speech.