Model adaptation methods for speaker verification

Model adaptation methods for a text-dependent speaker verification system are evaluated. The speaker verification system uses a discriminant model and a statistical model to represent each enrolled speaker. These modeling approaches consist of a neural tree network and Gaussian mixture model. Adaptation methods are evaluated for both modeling approaches. We show that the overall system performance with adaptation is comparable to that obtained by training the model with the additional information. However, the adaptation can be performed within a fraction of the time required to retrain a model. Additionally, we have evaluated the adapted and non-adapted models with data recorded six months after the initial enrolment. The adaptation reduced the error rate for the aged data by 40%