Speech Recognition - Project Idea
A few notes regarding your approach and detailed questions:


First, it is very common in audio analysis to split the signal to be analyzed into short overlapping windows, because audio signals are not stationary (their characteristics change over time), so they need to be processed on short segments over which they can be considered stationary (many analyses assume stationarity). Typical segment size is in the 5 .. 20 ms range. You'll be left with an odd-size segment at the end, just pad it with zeros and that's it.
In speech recognition the most widely features are MFCC. AR coefficients are quite common in older literature too. Zero crossing rate or energy are unlikely to discriminate phonemes in a robust way.


Now, regarding your project...

First, it is not clear if the goal of your project is to arrive at a working system even if you have to reuse existing code for that, or to do it yourself to learn on the road...

If you want to reach the goal without learning anything, there are ready to use speech recognition packages like Sphinx or HTK - the catch is that to understand these tools one actually has to know a lot about speech recognition tech. The setup time (training plans, model topology, grammar) and the concepts can be daunting.

An intermediate level would be to use third party code for signal analysis and feature extraction, and then focus on the recognition itself. You'll find C/C++ MFCC or AR modeling code in audio feature extraction packages like YAAFE or Aubio. You can also use the feature extraction command line utility of HTK (HCopy with the right config file for extraction + HList to get a clear text file).

When designing a speech recognition system, you have to ask yourself these questions:


What is the vocabulary size (from discriminating 2 words to recognizing natural language)?
What is the speaker variability (will I have to recognize from the same speaker again and again, or will I have to deal with many different speakers)?
What kind of training data will I have, in particular, is it possible to collect labelled speech samples from the same voice as the one you'll have to recognize?
What are the conditions (clear recordings... or voice through a glitchy mobile phone with background street noises)?


You have only answered the first question. If the answer to the other ones are these ideal conditions: the system will only deal with one speaker + will have a corpus of a few recordings of yes/no uttered by this same speaker + there will be no background noise ; the problem is not that hard. And these ideals conditions are not that unrealistic - there are some voice control PC applications which require this kind of prior training, the user needs to pronounce a few occurrences of each command word.

In these ideal conditions, a rustic approach (early 80s tech), but which will work, is DTW. Extract from each utterance in the training corpus (which will be labelled either "yes" or "no") a sequence of feature vectors, and compute the DTW distance between the utterance you need to recognize and those in the training corpus. Use this distance metric and a "k-nearest neighbour" scheme to decide on the label. DTW is archaic compared to a proper acoustic model + word model + language model graph, but for a very simple vocabulary (just a handful of words) it does the job.

Now, if you want something speaker independent (which will work for many speakers, including those for which we don't have a template), you'll have three plans. One is to stay with the same approach and record utterances from a very diverse set of speakers in terms of gender, accent, hoping that it will cover enough ground. But DTW scales very poorly! Another is to use an adaptation scheme - find the transform of your feature vectors that minimize the sum of DTW distance (or the minimum DTW distance) between your utterance and the database (paradoxically, I'm sure you'll find more literature about adaptation in the context of classic HMM-based recognition instead of DTW). The last approach is to forego DTW and do the real thing (phone-level GMM + word + language model).

I won't be as negative as the other members. I used to give a very similar project (digit recognition) to students which had no prior experience with audio (but a semester of signal processing and prior experience with Matlab + some background literature before starting the project), and they completed it in Matlab in two or three 3 hours lab sessions (roughly one for MFCC computation, one for DTW, one for the whole corpus management and k-NN + evaluation routines) with about 100 lines of code in total - so this is not an unimaginable engineering feat requiring a full team of PhDs...