hidden Markov model

In automatic SPEECH RECOGNITION, an approach which uses a SPECTRAL model of a word, viewed as a Markov model of the ACOUSTIC event. The pronunciation of a word, in all its variant forms, can be seen as a stochastic process: that is, in a sequence of events (pronunciation here being modelled as a sequence of ‘slices’ through a speech SPECTROGRAM), the probabilities at each step depend on the outcome of previous steps. Each time the process is applied to the word, it generates a slightly different acoustic specification, within the limits of the model. Once a speech recognizer has been provided with Markov models for the words it contains, it can use these to evaluate the properties of a fresh speech event. When someone speaks a word into the recognition system, the acoustic event can be treated as if it were the output of a ‘hidden’ Markov model. The output of the model is known (i.e. the event), but not the model itself (i.e. it is hidden), and the job of the recognizer is to reconstruct it.