Text-dependent speaker recognition using vector quantization

An application of source coding to speaker recognition is described. The method is text-dependent - the text spoken is known, and the problem is to determine who said it. Each speaker is represented by a sequence of vector quantization codebooks; known input utterances are classified using these codebook sequences and the resulting classification distortion is compared to a rejection threshold. On a 16 speaker test population with an additional 111 imposters, this method achieved a false rejection rate of 0.8%, an imposter acceptance rate of 1.8%, and within the 16 speakers, an identification error rate of 0.0%.