An efficient scoring algorithm for Gaussian mixture model based speaker identification

This article presents a novel algorithm for reducing the computational complexity of identifying a speaker within a Gaussian mixture speaker model framework. For applications in which the entire observation sequence is known, we illustrate that rapid pruning of unlikely speaker model candidates can be achieved by reordering the time-sequence of observation vectors used to update the accumulated probability of each speaker model. The overall approach is integrated into a beam-search strategy and shown to reduce the time to identify a speaker by a factor of 140 over the standard full-search method, and by a factor of six over the standard beam-search method when identifying speakers from the 138 speaker YOHO corpus.