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.
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)