EFFICIENT SPEAKER IDENTIFICATION USING SPEAKER MODEL CLUSTERING

In large population speaker identification (SI) systems, like-lihood computations between an unknown speaker’s feature set and the registered speaker models can be very time-consuming and impose a bottleneck. For applications requir-ing fast SI, this is a problem. In prior work, we proposed the use of clusters of speaker models so that during the test stage, only a small proportion of speaker models in selected clusters are used in the likelihood computations resulting in a speed-up of 2 × without loss in accuracy. In this paper, we improve the method by incorporating log-likelihoods into the initial clustering as well as cluster selection. The new method al-lows for fewer clusters to be searched and thus higher speed-up factors while still maintaining acceptable accuracy levels. 1