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Multiple-language phone recognition and n-gram language modeling produce the best performance in formal language identification (LID) evaluations, but this method needs many kinds of data that were phonetically transcribed utterances. This phone-based system isn't easily to apply for dialects or minority languages identification, because it is difficulty to obtain kinds of utterances which were orthographically...
The baseline system PRLM has the best performance on NIST language recognition evaluation tasks. But this system needs orthographically or phonetically transcribed utterances which can not be easily obtained from Chinese dialects and minority languages. So, the PRLM system is not used to these languages. To overcome this limitation, we present the Gaussian mixture model recognizer followed by language-dependent...
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