Efficient language identification using anchor models and support vector machines

Abstract

Anchor models have been recently shown to be useful for speaker identification and speaker indexing. The advantage of the anchor model representation of a speech utterance is its compactness (relative to the original size of the utterance) which is achieved with only a small loss of speaker-relevant information. This paper shows that speaker-specific anchor model representation can be used for language identification as well, when combined with support vector machines for doing the classification, and achieve state-of-the-art identification performance. On the NIST-2003 Language Identification task, it has reached an equal error rate of 4.8% for 30 second test utterances. © 2006 IEEE.