Text-dependent speaker verification using data fusion
Kevin R. Farrell · 2002
A new system is presented for text-dependent speaker verification. The system uses data fusion concepts to combine the results of distortion-based and discriminant-based classifiers. Hence, both intraspeaker and interspeaker information are utilized in the final decision. The distortion and discriminant-based classifiers are based on dynamic time warping (DTW) and the neural tree network (NTN), respectively. The system is evaluated with several hundred two word utterances collected over a telephone channel. The combined classifier yields an equal error rate of two percent for this task, which is better than the individual performance of either classifier.