Hidden Markov Models - Identifying Phonemes 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 :)!
You should first appreciate why an HMM would be useful. The problem that you've described is classify a given utterance as either a yes or a no (it would be also useful to have a "neither" class). Because speech is a time-evolving quantity, it needs a classifier that is able to deal with sequences of feature vectors. Different people say "yes" or "no" with different speeds. For example, I might say "no" and the speech signal could be 200ms long or you could say "no" and the speech signal could be 50ms long. 

You might want to look into creating windows of a fixed length for your speech signal and extracting certain transform domain coefficients (like FFT, DCT etc.) from it and use these to train an HMM. Alternatively, the mel-frequency cepstrum coefficients (MFCCs) are also very popular in training phoneme based HMMs for speech recognition.