Quasi-Factorial Prior for i-vector Extraction.

We analyze the i-vector extraction from the perspective of the prior distribution exerted on the mean supervector of Gaussian mixture model (GMM). To this end, we start off with the analysis of the subspace prior which leads to the compressed representation in the standard i-vector extraction. We then propose the use of quasi-factorial prior and show how it impacts the total variability space and its application for i-vector extraction. The quasi-factorial prior could be used in a standalone manner, or in combination with a subspace prior. In the latter context, we found that the performance of the standard i-vector can be greatly improved with the use of quasi-factorial prior followed by a subspace prior. This assertion is confirmed through experiments conducted on the NIST 2010 Speaker Recognition Evaluation (SRE10) dataset. [ABSTRACT FROM PUBLISHER]