Probabilistic linear discriminant analysis of i-vector posterior distributions

Abstract

The i-vector extraction process is affected by several factors such as the noise level, the acoustic content of the observed features, and the duration of the analyzed speech segment. These factors influence both the i-vector estimate and its uncertainty, represented by the i-vector posterior covariance. This paper present a new PLDA model that, unlike the standard one, exploits the intrinsic i-vector uncertainty. Since short segments are known to decrease recognition accuracy, and segment duration is the main factor affecting the i-vector covariance, we designed a set of experiments aiming at comparing the standard and the new PLDA models on short speech cuts of variable duration, randomly extracted from the conversations included in the NIST SRE 2010 female telephone extended core condition. Our results show that the new model outperforms the standard PLDA when tested on short segments, and keeps the accuracy of the latter for long enough utterances. In particular, the relative improvement is up to 13% for the EER, 5% for DCF08, and 2.5% for DCF10.