The aim of Automatic Speaker Verification (ASV) is to detect whether a speech segment has been uttered 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.
Cite as: Blouet, R., Mokbel, C., Mokbel, H., Soto, E.S., Chollet, G., Greige, H. (2004) BECARS: a free software for speaker verification. Proc. The Speaker and Language Recognition Workshop (Odyssey 2004), 145-148
@inproceedings{blouet04_odyssey,
author={Raphael Blouet and Chafic Mokbel and Hoda Mokbel and Eduardo Sánchez Soto and Gérard Chollet and Hanna Greige},
title={{BECARS: a free software for speaker verification}},
year=2004,
booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2004)},
pages={145--148}
}