Hidden Markov Models - Identifying Phonemes
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.