Composite background models and score standardization for language identification systems

This paper describes two enhancements to our language identification system. Composite background (CBG) modeling allows us to identify target language speech in an environment where labeled background training data is unavailable or limited. Instead of separate models for each of the background languages, a single composite background model is created from all the non-target training speech. Generally, the CBG system performed about as well as a baseline system containing a separate model per background language. The average equal error rate for 12 CBG tests was 13.6% versus 13.4% for the baseline. We have also developed and tested a standardized confidence scoring function based on a single-layer perceptron which has proven to be capable of robust modeling of score distributions.