Model transformation for robust speaker recognition from telephone data

In the context of automatic speaker recognition, we propose a model transformation technique that renders speaker models more robust to acoustic mismatches and to data scarcity by appropriately increasing their variances. We use a stereo database containing speech recorded simultaneously under different acoustic conditions to derive a synthetic variance distribution. This distribution is then used to modify the variances of other speaker models from other telephone databases. The technique is illustrated with experiments conducted on a locally collected database and on the NIST´95 and ´96 subsets of the Switchboard corpus