Frame pruning for speaker recognition

In this paper, we propose a frame selection procedure for text-independent speaker identification. Instead of averaging the frame likelihoods along the whole test utterance, some of these are rejected (pruning) and the final score is computed with a limited number of frames. This pruning stage requires a prior frame level likelihood normalization in order to make comparison between frames meaningful. This normalization procedure alone leads to a significant performance enhancement. As far as pruning is concerned, the optimal number of frames pruned is learned on a tuning data set for normal and telephone speech. Validation of the pruning procedure on 567 speakers leads to a 27% identification rate improvement on TIMIT, and to 17% on NTIMIT