Dealing with Diverse Data Variances in Factor Analysis Based Methods

Abstract

Probabilistic Linear Discriminant Analysis (PLDA) and the concept of i-vectors are state-of-the-art methods used in the speaker recognition. They are based on Factor Analysis, in which a data covariance matrix is decomposed in order to find a low dimensional representation of given feature vectors. More precisely, the Factor Analysis based methods seek for directions/subspaces in which the projected (overall/between/within) variance is highest. In order to train models related to individual methods, development speech corpora comprising various acoustic conditions are utilized. The higher are the variations in some of these acoustic conditions, the more will the model tend to reflect them. Strong data variations in some of the development corpora may suppress conditions present in other corpora. This can lead to poor recognition when acoustic variations in test conditions significantly differ. In this paper techniques alleviating such effects are investigated. The idea is to use several background and i-vector models related to different parts of development data so that several i-vectors are extracted, processed and handed over to the PLDA modelling. PLDA model is then used to utilize all the extracted information and provide the verification result.