Efficient speaker identification using speaker model clustering

In large population speaker identification (SI) systems, likelihood computations between an unknown speaker´s feature set and the registered speaker models can be very time-consuming and impose a bottleneck. For applications requiring 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 speedup 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 allows for fewer clusters to be searched and thus higher speed-up factors while still maintaining acceptable accuracy levels.