Dynamic Time Warping as a classifier, a good idea? - signal-processing

Question

Before you start reading please forgive me for the bad English, thanks.
I am in my final year in computer engineering course in Libya.
my graduation project name is "Speech Recognition System for isolated words using classifier fusion method".
the basic idea of the project is, I input a 1sec recording of a number (0-9), and it gets displayed on the screen as text.
My steps are:
* Input the word .
* Pre-processing of the speech signal.
* Extract features using Mel Frequency Cepstral Coefficients.
* classify the word using:
* MED Classifier.
* Dynamic Time Warping Classifier .
* Bayes Classifier .
* Classifier Fusion: Combination of the above classifiers, hoping to compensate for weak
classier performance.
So after I used MFCC and extracted my features , I used the MED just to have a look at the whole ASR system a visualize how it should work.
Then I started with the DTW classifier, and to be honest I am not sure I am doing it right, so here is the code and if anyone ever used DTW as a classifier before please tell me is it a good idea using DTW, and if so, am I doing it right???
test.mat has two variables in it 'm' is the spoken word of the number one, 'b' is the spoken word of the number one also but every one was recorded alone, i will then keep 'm', and compare it to the recorded word two, the cost of 1vs1 must be smaller then 1vs2, but not in my case, why is that????
clear;
load('test.mat')
b=m;
m=b;
dis=zeros(length(m),length(b));
ac_cost=zeros(length(m),length(b));
cost=0;
p=[];
%we create the distance matrix by calculating the Eucliden distance between
%all pairs
for i = 1 : length(m)
for j = 1 : length(b)
dis(i,j)=(b(j)-m(i))^2;
end
end
ac_cost(1,1)=dis(1,1);
%calculate first row
for i = 2 : length(b)
ac_cost(1,i)=dis(1,i)+ac_cost(1,i-1);
end
%calculate first coulmn
for i = 2 : length(m)
ac_cost(i,1)=dis(i,1)+ac_cost(i-1,1);
end
%calculate the rest of the matrix
for i = 2 : length(m)
for j = 2 : length(b)
ac_cost(i,j)=min([ac_cost(i-1,j-1),ac_cost(i-1,j),ac_cost(i,j-1)])+dis(i,j);
end
end
%find the best path
i=length(m)
j=length(b)
cost=cost+dis(i,j)+dis(1,1)
while i>1 && j>1
cost=cost+min([dis(i-1, j-1), dis(i-1, j), dis(i, j-1)]);
if i==1
j=j-1;
elseif j==1
i=i-1;
else
if ac_cost(i-1,j)==min([ac_cost(i-1, j-1), ac_cost(i-1, j), ac_cost(i, j-1)])
i=i-1;
elseif ac_cost(i,j-1)==min([ac_cost(i-1, j-1), ac_cost(i-1, j), ac_cost(i, j-1)])
j=j-1;
else
i=i-1;
j=j-1;
end
end
end
Thank you all in advance