Using search to improve hidden Markov models

The authors explore the use of randomized performance-based search strategies to improve the generalization of hidden Markov models (HMMs) in a speaker-independent automatic speech recognition system. No language models are used, so that the performance of the unit models themselves can be compared. Simulated annealing and random search are applied to several components of the system, including phoneme model topologies, distribution tying, the clustering of allophonic contexts, and the sizes of mixture densities. By using knowledge of the speech problem to constrain the search appropriately, both reduced numbers of parameters and better phoneme recognition are obtained, as experimentally demonstrated on the TIMIT corpus. The clusters developed here automatically introduced a useful degree of generalization. The performance increase was preserved when the allophone distributions were tied to parallel transitions in a small context-independent model set. In this way models for phoneme classes can be built with which upper bounds for scores of detailed phoneme models can be obtained.<>