EOARD - Improving the capacity of language recognition systems to handle rare languages using radio broadcast data

Current situation in language recognition
The last editions of NIST Language recognition (LRE) evaluations have shown
substantial improvement in the performance of LRE systems. Both acoustic and
phonotactic approaches have reached a certain maturity in both the actual
modeling of target languages and coping with the adverse influences of changing
channel. There are several ways how to further improve the current LRE systems
and some of them were investigated in the Brno University of Technology (BUT)
2007 submission to this evaluation, for example:

   - discriminative training and channel compensation techniques for both
     acoustic and phonotactic modeling.
   - use of large vocabulary continuous speech recognition (LVCSR) with following
     confidence measures.
However, with all this beautiful science, we are still facing the old problem of
any recognizer's training and testing: the lack of data. While it is easy to
train and test an LRE system for languages with established speech and language
resources, such as English, Mandarin, etc., rare languages lack these standard
resources. Consider the example of Thai: this language is spoken by 65 million
speakers, but for the NIST 2007 LRE evaluations, we disposed only of less than 2
hours distributed by NIST as part of the development package, although we have
contacted several Thai speech processing labs - a large spontaneous telephone
database for this language simply does not exist.

The proposed solution
This proposal aims at filling this gap by using the data acquired from public
sources, namely radio broadcasts. This approach (which is pretty intuitive and we
do not declare Speech@FIT to be the only place having this idea) should provide
us with plenty of data that we believe will lead to:

   - improved performance for known languages.
   - ability to process languages that were so far excluded because of
     unavailability of data.
This approach is however far from "we record the data, push a button and will
have a much better LRE system within a month". There is significant amount of
work especially on the selection of data and channel normalization.