User-customized Password Speaker Verif Gmm Model

In this paper, we present a new approach towards user-customized password speaker verification combining the advantages of hybrid HMM/ANN systems, usingArtificial Neural Networks (ANN) to estimate emission probabilities of Hidden Markov Models , and Gaussian Mixture Models. In the approach presented here, we indeed exploit the properties of hybrid HMM/ANN systems, usually resulting in high phonetic recognition rates, to automatically infer the baseline phonetic transcription (HMM topology) associated with the user customized password from a few enrollment utterances and using a large, speaker independent, ANN. The emission probabilities of the resulting HMMs are then modeled in terms of speaker specific/adapted multi-Gaussian HMMs or speaker specific/adapted ANN. In the proposed approach, the hybrid HMM/ANN system is used as a model for utterance (password) verification, while still using a speaker independent GMM for speaker verification. Results (EER) are compared to a state-of-the-art textdependent approach, using multi-Gaussian HMMs only.