Comparison of whole word and subword modeling techniques for speaker verification with limited training data

In this paper we use whole word and subword hidden Markov models for text dependent speaker verification. In this application usually only a small amount of training data is available for each model. In order to cope with this limitation we propose a intermediate functional representation of the training data allowing the robust initialization of the models. This new approach is tested with two databases and is compared both with standard training techniques and the dynamic time warp method. Secondly, we give results for two types of subword units. The scores of these units are combined in two different ways to obtain word error rates