Training of HMM recognizers by simulated annealing

Hidden Markov models (HMM) are the basis for some of the more successful systems for continuous and discrete utterance speech recognition. One of the reasons for the success of these models is their ability to train automatically from marked speech data. The currently known forward-backward and gradient training methods suffer from the problem that they converge to a local maximum rather than to the global maximum. Simulated annealing is a stochastic optimization procedure which can escape a local optimum in the hope of finding the global optimum when presented with a system which contains many local optima. This paper shows how simulated annealing may be used to train HMM systems. It is experimentally shown to locate what appears to be the global maximum with a higher probability than the forward-backward algorithm.