ON THE APPLICATION OF MIXTURE AR HIDDEN MARKOV-MODELS TO TEXT INDEPENDENT SPEAKER RECOGNITION

Linear predictive hidden Markov models have proved to be an efficient way for statistically modeling speech signals. The possible application of such models to statistical characterization of the speaker himself is described and evaluated. The results show that even with a short sequence of only four isolated digits, a speaker can be verified with an average equal-error rate of less than 3%. These results are slightly better than the results obtained using speaker dependent vector quantizers, with comparable numbers of spectral vectors. The small improvement over the vector quantization approach indicates the weakness of the Markovian transition probabilities for characterizing speaker dependent transitional information.