BECARS : a free software for speaker verification

Abstract

The aim of Automatic Speaker Verification (ASV) is to detect whether a speech segment has been ut-tered by the claimed identity or by an impostor. Our contribution includes the distribution of BECARS , a free software based on Gaussian Mixture Models (GMM) for Automatic Speaker Verification (ASV), and the design of a new methodology to estimate the decision score in an ASV system. BECARS in available at http://www.tsi.enst.fr/¤ blouet/Becars/. The main characteristic of this software is to allow the use of several adaptation techniques including the most common ones such as Maximum A Posteriori (MAP) and Maximum Likelihood Linear Regression (MLLR). The proposed method for score computation is based on the use of a hierarchical Gaussian clusterization method that we describe in details in this paper. We introduce this work with a general summary of Automatic Speaker Verification (ASV), followed by a description of the adaptation technique available in BECARS used in this work. We then present and evaluate our score computation scheme before concluding the paper.