Compatible probability measures of the outputs of template-based speaker identification classifiers for data fusion

Different approaches have been proposed for speaker identification (SI). Distortion outputs of template-based SI are generally in compatible with probability measures. Frequently, data fusion is used for SI that uses the two kinds of distortion measures, which give rise to incompatibility problems. A technique, which converts the distortion outputs of template-based SI classifiers into compatible probability measures at the same scale for the SI data fusion problem at the measurement level, is presented. In the proposed approach, for each template-based classifier, the stochastic model for each distortion output of the classifier and each speaker, given that the unknown utterance comes from this speaker, is estimated. Then, a posteriori probability of the unknown utterance belonging to each speaker is calculated for each given distortion output. Compatible probability measures of the distortion outputs are obtained based on the posteriori probabilities. Experimental results confirm the effectiveness of the proposed approach for SI data fusion at the measurement level.