Speaker verification using phoneme-based neural tree networks and phonetic weighting scoring method

A text-dependent speaker verification system based on neural tree network (NTN) phoneme model and phonetic weighting scoring method is presented. The system uses a set of concatenated NTNs trained on phonemes to model a password. In contrast to the conventional stochastic approaches which model the phonemes by hidden Markov models (HMMs), the new approach utilizes the discriminative training scheme to train a NTN for each phoneme. The phoneme-based NTN is trained to discriminate the phoneme spoken by the speaker with respect to those spoken by other speakers. A weighted scoring method depending on the phoneme´s ability for speaker verification is used to improve the performance. The proposed system is evaluated by experiments on the YOHO database. Performance improvements are obtained over conventional techniques