Speaker verification using frame and utterance level likelihood normalization

In this paper, we propose a new method, where the likelihood normalization technique is applied at both the frame and utterance levels. In this method based on Gaussian Mixture Models (GMM), every frame of the test utterance is inputed to the claimed and all background speaker models in parallel. In this procedure, for each frame, likelihoods from all the background models are available, hence they can be used for normalization of the claimed speaker likelihood at every frame. A special kind of likelihood normalization, called Weighting Models Rank, is also proposed. We have evaluated our method using two databases - TIMIT and NTT. Results show that the combination of frame and utterance level likelihood normalization in some cases reduces the equal error rate (EER) more than twice.