On combining classifiers for speaker authentication ?
Speaker verification and utterance verification are examples of techniques that can be used for speaker authentication purposes. Speaker verification consists of accepting or rejecting the claimed identity of a speaker by processing samples of his/her voice. Usually, these systems are based on HMM models that try to represent the characteristics of the speakers vocal tracts. Utterance verification systems make use of a set of speaker-independent speech models to recognize a certain utterance. If the utterances consist of passwords, this can be used for identity verification purposes. Up to now, both techniques have been used separately. This paper is focused on the problem of how to combine these two sources of information. New architectures are presented to join an utterance verification system and a speaker verification system in order to improve the performance in a speaker verification task.
speaker authentication speaker verification utterance authentication gaussian mixture models verbal information verification neural networks classifier combination