A subword neural tree network approach to text-dependent speaker verification

A new algorithm for text-dependent speaker verification is presented. The algorithm uses a set of concatenated neural tree networks (NTNs) trained with subword units for speaker verification. The conventional NTN has been found to provide good performance in text-independent tasks. In the new approach, two types of subword unit are investigated, phone-like units (PLUs) and HMM state-based units (HSU's). The training of the models includes several steps. First, the predetermined password in the training data is segmented into subword units using a hidden Markov model (HMM) based segmentation method. Second, an NTN is trained for each subword unit. The new structure integrates the discriminatory ability of the NTN with the temporal models of the HMM. This new algorithm was evaluated by experiments on a TI isolated-word database, and YOHO database. An improvement of performance was observed over the performance obtained using a conventional HMM.