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. 1.

In this paper we describe experiments conducted during the development of a lattice-based PPRLM language identification system as part of the NIST 2005 language recognition evaluation campaign. In experiments following LRE05 the PPRLM-lattice sub-system presented here achieved a 30s/primary condition EER of 4.87%, making it the single best performing recognizer developed by the MIT-LL team. Details of implementation issues and experimental results are presented and interactions with backend score normalization are explored. 1 1.