Speaker recognition by means of Deep Belief Networks

Most state–of–the–art speaker recognition systems are based on Gaussian Mixture Models (GMMs), where a speech segment is represented by a compact representation, referred to as “identity vector” (ivector for short), extracted by means of Factor Analysis. The main advantage of this representation is that the problem of intersession variability is deferred to a second stage, dealing with low-dimensional vectors rather than with the high-dimensional space of the GMM means. In this paper, we propose to use as a pseudo-ivector extractor a Deep Belief Network (DBN) architecture, trained with the utterances of several hundred speakers. In this approach, the DBN performs a non-linear transformation of the input features, which produces the probability that an output unit is on, given the input features. We model the distribution of the output units, given an utterance, by a reduced set of parameters that embed the speaker characteristics. Tested on the dataset exploited for training the systems that have been used for the NIST 2012 Speaker Recognition Evaluation, this approach shows promising results.