Efficient speaker search over large populations using kernelized locality-sensitive hashing

We propose a novel method of efficiently searching very large populations of speakers, tens of thousands or more, using an utterance comparison model proposed in a previous work. The model allows much more efficient comparison of utterances compared to the traditional Gaussian Mixture Model(GMM)-based approach because of its computational simplicity while maintaining high accuracy. Furthermore, efficiency can be drastically improved when approximating searches using kernelized locality-sensitive hashing (KLSH). From a speaker's utterance, a set of statistics are extracted according to the utterance comparison model and converted to a set of hash key bits. An Approximate Nearest Neighbor search using the Hamming Distance can be done to find candidate matches with the query speaker, which are then rank-ordered by linearly comparing them with the query using the utterance comparison model. Compared to GMM-based speaker identification and some of its variants that have been proposed to increase its efficiency, the proposed KLSH-based method is orders of magnitude faster while compromising a negligible amount of accuracy for sufficiently long query utterances. At a more fundamental level, we also discuss how our speaker matching framework differs from the traditional Bayesian decision rule used for speaker identification.