Hi Aly
Thanks a lot for your kind words, they go straight to my heart!
The idea of the "universal model" is to represent an average of all possible voices so we can calculate a relative distance and provide a ratio, so yes, one need to use as many different voices as possible, using a single voice is counter productive.
Provided you have plenty of them, in order to reduce the number of false positives while verifying, you may want to use a subset of all the voices available and select the voices that are closer to the one you're identifying. E.g. grouping males and females and creating a UM for each, though some men sound like women and the other way round so I'd hand pick an initial group and measure the distance programmatically to be able to tell which group any new voice belongs to. There can be more groups than 2 (groups are actually called "cohorts" in speech terminology).
Something else, I hope to be improving the algorithm so keep your original samples around so you can recreate the voice prints using the new algo when it's out :-)
HTH
Cheers
Amaury