Exploiting Support Vector Machines for Speaker Verifi
نویسنده
چکیده
Hidden Markov Models have been proved to be an efficient way for statistically modeling sequence signals. And the Support Vector Machines seem to be a promising candidate to perform the classification task. A new method combining support vector machine and hidden Markov models is proposed. The output of support vector machines are modified as posterior probability using sigmoid function, and act as a probability evaluator in the hidden states of HMM.
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