Improving language recognition with multilingual phone recognition and speaker adaptation transforms

Abstract

We investigate a variety of methods for improving language recognition accuracy based on techniques in speech recognition, and in some cases borrowed from speaker recognition. First, we look at the question of language-dependent versus language-independent phone recognition for phonotactic (PRLM) language recognizers, and find that language-independent recognizers give superior performance in both PRLM and PPRLM systems. We then investigate ways to use speaker adaptation (MLLR) transforms as a complementary feature for language characterization. Borrowing from speech recognition, we find that both PRLM and MLLR systems can be improved with the inclusion of discriminatively trained multilayer perceptrons as front ends. Finally, we compare language models to support vector machines as a modeling approach for phonotactic language recognition, and find them to be potentially superior, and surprisingly complementary.