This is a question that came to mind as a result of a previous question Hidden Markov Models - Distinct Observation Symbols and subsequent answer from @pichenettes.
One approach to speech recognition is to use Hidden Markov Models (HMM) to identify patterns in speech. Both discrete HMM models and continuous HMM models have been used, but the continuous HMM approach seems to produce better results.
In continuous approach, the “input” to the HMM models is a series of vectors constructed from frames of processed speech. So speech as processed in blocks of some time length resulting in vectors of some fixed dimension say N, with the size of N being directly related to the frame length.
My question is this:
Is there a relationship between the frame length (or alternatively the dimension of the vectors, N) and the number of states resulting in the HMM models used for recognition?
Also is there a relationshiop between the number of HMM models required to predic speech and the size of N?