I'm developing a project that identifies Phonemes to be able to identify whether someone is saying either "Yes" or "No".
So far in the project, I have used Zero-crossings to identify what the person is saying, this works really well and seems simple enough to understand. The project, however, needs a few enhancements and has to be developed using a Hidden Markov Model.
My question is this:
I want to develop a Hidden Markov Model, without erasing the work that I have already completed. I.e. I strip the data that do not warrant consideration by counting the number of zero-crossings as well as the summation of the blocks.
I do not understand what data I would need to train the HMM in order to be able to identify these Phonemes. E.g.
With Zero-crossings I have identifies that:
Yes - Zero-crossings start low and then the value increases
No - Zero-crossings start low and then do not increase with value.
Could I train my HMM algorithm so that it interprets these values?
Or could anyone suggest a method of which I can train the HMM to be able to identify the word that is inputted in the sample?
Hope someone can help :)!