Speech Analysis - Zero-Crossing
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.