Use of Anti-Models to Further Improve State-of-the-Art PRLM Language Recognition System

This paper concentrates on PRLM (phoneme recognizer followed by language model) approach to language recognition. It elaborates on our prior work concerning the quality of phoneme recognition and amounts of training data for phoneme recognizer training. It reports improvements brought to our PRLM system by better phoneme recognition and Witten-Bell discounting in LM-modeling. The paper then concentrates on the use of phoneme lattices and anti-models. Training and scoring on phoneme lattices brought significant improvement in language recognition accuracy. The antimodels are simple, yet powerful technique to improve the discrimination between target and non-target languages. All results are reported on standard NIST 2003 data; comparison with other published results is favorable to our system.