We’ll see in modules 9-10 that a finite state language model very easy to combine with HMMs. In your report, explain why this is.
The language model computes the term P(W) – the probability of the word sequence W. We’re actually going to use what appears to be a non-probabilistic model, in the form of a simple grammar. To think of this in probabilistic terms, we can say that it assigns a uniform probability to all allowed word sequences, and zero probability to all other sequences.
Why do we need a model of word sequences when we are doing isolated digit recognition?
HTK has tools to help write grammars manually, then convert them into finite state models. A grammar for isolated digits, and the corresponding finite state model, is provided for you in the resources directory, called grammar; have a look at it. The finite state model is in grammar_as_network.
The HTK manual contains all the information you need to understand the simple isolated word grammar, and how to extend that to connected digits.
Later, if you do the connected digits experiment, you will write a new grammar, from which you can automatically create a finite state network version using the HParse tool.