Efficient Language Identification using Anchor Models and Support Vector Machines
Εlad Noor, Hagai Aronowitz · 2006
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