Estimation of prior probabilities in speaker recognition

According to Bayesian decision theory, the maximum a posteriori (MAP) decision rule is used to minimize the speaker recognition error rate. The a posteriori probability is determined if the a priori probability and the likelihood function are known. However, there has been no method to determine the a priori probability, therefore the maximum likelihood (ML) decision rule is used instead. The paper proposes a method to estimate the a priori probability for speakers based on a training data set and speaker models. Speaker identification experiments performed on 138 Gaussian mixture speaker models in the YOHO database using the MAP rule showed lower error rates than using the ML rule.