Using MFCC in spoken words recognition
Do we really need 10 samples of each word? or can we "combine" them into a single template? and if so, how?


You can use single template, 10 templates will make result more accurate.


  Should we run the SVM directly on the MFCCs vectors? On the MFCC vector of vectors (for each word)? On the DTW values? or should we combine all the MFCC vectors into a single one?


SVM has no meaning for your application because it is not invariant to time stretch so it's not suitable for classification of time series. There are SVM modifications to deal with time series, for example

Hidden Markov Support Vector Machines

but it's completely different story


  If we should look for a minimal match for the DTW between the input and the templates, should we give different weights to the templates depending on length, or even account for the differences between the templates (the templates of the word "1" are more distant from one another than the templates of the word "2").


No, it's not necessary. A simple comparison of DTW values should work.


  All three of these methods didn't work well (and gave almost everything wrong results).


Probably you made a mistake in DTW implementation first of all. You need to verify if you implemented everything correctly. You might make a mistake in DTW and go to incorrect conclusion


  or references to good sources.


Read a good text book. At least Rabiner "Fundamentals of Speech Recognition"