Model combination and weight selection criteria for speaker verification

We focus on the score combination for three separate modeling approaches as applied to text-dependent speaker verification. The modeling methods that are evaluated consist of the neural tree network (NTN), hidden Markov model (HMM), and dynamic time warping (DTW). One of the main challenges in combining scores of several models is how to select the weight for each model. One method is to use equal weights for all models used in the combination. Another method is to use the Fisher linear discriminant to select the weights that maximize the ratio of the separation of the inter-class means to the sum of the variances. Both methods are evaluated for three separate databases and the results are compared to the optimal performance as obtained by an exhaustive search over the weight space. Overall, both weight selection methods provide performance close to the optimal point. It is also shown that the optimal combination of three models provides lower error rates than that achievable with two models.