Predicting, diagnosing and improving automatic language identification performance

Language-identification (LID) techniques that use multiple single-language phoneme recognizers followed by n-gram language models have consistently yielded top performance at NIST evaluations. In our study of such systems, we have recently cut our LID error rate by modeling the output of n-gram language models more carefully. Additionally, we are now able to produce meaningful confidence scores along with our LID hypotheses. Finally, we have developed some diagnostic measures that can predict performance of our LID algorithms.