They devices which vary considerably in sophistication, but which essentially are involved in some kind of processing of the incoming signal. Whether we are using HMMs or some other method of discriminating within the signal, there is much to be gained by subjecting the output of the recognition process to a grammar. States and their ordering within the models An HMM is a model or representation of a surface acoustic unit in terms of the sequencing of states in a finite state device. If we were modelling a word, for example, we might decide that we would need, say, twenty of such states for a necessary representation of the word. Thus early states in the model capture a representation of the allophones at the beginning of the word, and the latest states in the model capture the final allophones in the word.
Progress is always from left to right.
In a first-order Markov model of the kind used in asr we are concerned only with transitions between two states at any one time. This is a statistical process which is capturing the probability of the device switching to a particular state dependent only on the immediately prior state. Take as an example an HMM for a single allophone and assume that we are representing the entire acoustic signal. Fine tuning of the representation is essential to optimise the use of such model descriptions. In considering a match with an incoming sound it would be the case that the likelihood of a match would be determined by the model observation probabilities.