Speech Analysis - Zero-Crossing Basically, (I know I've asked a simular question, but I have been away and really thought about it this time)!

Let me try and explain this in the best way possible:

Basically, I'm attempting to create an algorithm that takes a voice sample (.wav)
of someone saying either "Yes", or "No".  Now, in order to do this I am attempting to split the sample into equal blocks in order to get the Phones. 

Now, I have thought about this.. 

I don't need to technically analise all of Phones, I can identify if the the sample is either "Yes" or "No" by just looking for a "Y" Phone. Because if a "Y" doesn't exist then the sample is "No". So, I can handle this by implementing a decision tree. 

Ok, so to my question. I know that this can be achieved using Zero-Crossing, but, I don't have any experience in this (Ok, people are going to tell me to stop doing it then) but, we all had to learn somewhere, right? 

If someone could possibly link me to a website or a book where I can learn about the Zero-crossing method? Then it would be great. 

The main thing I don't understand about it is, how can Zero-crossing determine whether the Phone is a "Y"? Or, will I need to have some samples of "Y" in order to match through Correlation?, or something. 

I hope someone can advise me. 

Thanks :)
Zero-crossing rate (ZCR) might be useful for voiced/unvoiced frame discrimination, speech/music discrimination, but it is of much lesser importance in speech recognition. One reason is that it is pitch-dependent and not robust to background noise or hum. It is not difficult to craft very different signals (say a female voice saying a phone and a male voice saying another phone) that have the exact same ZCR; or to add some noise to a signal in a way that will half or double its ZCR.

Using zero-crossing rate might work in the single-speaker + prior training case. Collect a handful of samples of each words, compute the zero crossing rate, and train a naive bayesian classifier for this two-class problem. Be prepared to poor performances, and don't expect your system to generalize to other speakers.

The only plausible reason one would use such a feature would be on embedded systems with very limited computational power. Otherwise, there's no reason not to use "proven" features like MFCC or AR coefficients.