The following forum message was posted by mokhov at 
http://sourceforge.net/projects/marf/forums/forum/213052/topic/3782565:

Hi ShanTrip,

The type of application you describe should be quite easy to do,
you just have to change categories from speakers to words and
do some experiments to select which algorithms do it best for
this category. It is bound to be different than the best algos for
speakers themselves. For example, I\'ve done experiments on
changing categories to spoken accent and gender instead of
identity in here:

http://portal.acm.org/citation.cfm?id=1370256.1370262

The words principle is the same. The SpeakerIdentApp\'s .txt db
would have to become like a dictionary of word and the associated
\"good\" pronunciations files for training and testing. It may also be
wise to experiment to split each pronounced word into three subcategories,
one per age group and gender to increase accuracy. In this case,
a match would be searched in the related categories and them mapped
to the same spelled word when the smallest distance is found or
the largest similarity is found between the spoken and trained utterances.
Else the application is quite feasible and easy to alter from SpeakerIdentApp
to the task. Let me know if you still have any more questions.

-s

...  and it\'s not long at all ;-)

-s

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