Parallelized factor analysis and feature normalization for automatic speaker verification

Factor analysis (FA) is one of the key advances presented in recent speaker verification evaluations. This technique is able to successfully remove session variability effects and it is currently used in many state-of-the-art automatic speaker verification systems. This paper addresses several practical issues in using an FA model in order to speed up model training and to achieve good performance. A parallelized training algorithm as well as maximum-likelihood estimation are proposed for fast training. The front-end feature normalization techniques are also investigated in the context of FA model. We demonstrate that factor analysis is very robust, and can be successfully applied to various kinds of feature normalization. Moreover, the proposed parallelized MLE implementation speeds up the training procedure from several days to several hours without sacrificing the performance.