speech - Using DTW on the MFCC's to match a spoken word to a template

I am trying to code a simple algorithm in MATLAB that would be used to detect a single word. The goal would be to have a user record the word one time to act as a template, then make the user repeat the same word to try and detect it. Searching on this website has answered many of my questions, but I still have some interrogations.

So far my algorithm calculates the MFCC's in the following way:

• Extract the data from a .wav file (recorded at $8000\textrm{ Hz}$)


• Build frames of $64\textrm{ ms}$ with a $50\%$ overlap, applying a Hann window


• Calculate the FFT of all these frames


• Calculate the Power Spectrum using the FFT results ($P(k) = \lvert X(k)\rvert^2$)


• For each frame, extract 26 coefficients by multiplying the Power Spectrums with the 26 Mel filters and summing the results for a given Mel filter


• Calculate the $\log_{10}$ of the coefficients


• Apply a dct on a frame's 26 coefficients

My goal was to use the MFCC's of the template as well as the MFCC's of the repeated work and compare them using a DWT algorithm. My DWT algorithm is already programmed and functional.

However, my algorithm does not work very well and it seems like certain parameters affect the results quite a lot. Here are my questions:

1. 
   

   Are these steps enough to detect a spoken word?

   
   


2. 
   

   Would it be better to use a large number of fixed templates instead of having the user pre-record a template? Which method should result in better recognition performance?

   
   



3. 
   

   If the person repeats the word using a different distance from the microphone, the increased/decreased values of the MFCC's are enough to mess up the DTW results. Is there a smart way to normalize the MFCCs to try and cancel the effect of the microphone distance?

   
   


4. 
   

   Some websites recommend using only the 13 MFCC's with the smallest values.

   
   
   
   
   • Why is that?
   
   
   • Also, are they talking about the smallest magnitudes, or the smallest values?
   
   
   • Assuming 13 MFCC's are very big negative numbers, while 13 other MFCC's are small positive numbers, which set would I keep?
   
   
   
   
   

EDIT: Also, the first coefficient of each frame is always much bigger than the other coefficients. I would say it's magnitude is bigger my a factor of 100. Obviously, when I caculate the DTW using Euclidian distance, I'd say this coefficient is the only relevant one since it is so much bigger than all the other values. Should it be discarded?