EFFICIENT HIERARCHICAL LABELER ALGORITHM FOR GAUSSIAN LIK ELIHOODS COMPUTATION IN RESOURCE CONSTRAINED SPEECH RECOGNITION SY STEMS
This paper presents a new time/memory-efficient algorithm for the evaluation of state likelihoods in an HMM-based speech recognizer where the states are modeled by Gaussian Mixtures. We first present a fast hierarchical labeling scheme and then an improved version, which is specifically geared toward use in recognizers that use asynchronous sear ch (e.g., stack search) as opposed to synchronous (Viterbi) se arch. The improved labeling algorithm improves the speed of the hierarchical labeler by more than 30%.