Context-dependent phone models and models adaptation for phonotactic language recognition
Mohamed Faouzi BenZeghiba, Jean‐Luc Gauvain, Lori F Lamel · 2008
The performance of a PPRLM language recognition system depends on the quality and the consistency of phone decoders. To improve the performance of the decoders, this paper investigates the use of context-dependent instead of contextindependent phone models, and the use of CMLLR for model adaptation. This paper also discusses several improvements to the LIMSI 2007 NIST LRE system, including the use of a 4gram language model, score calibration and fusion using the FoCalMulti-class toolkit (with large development data) and better decoding parameters such as phone insertion penalty. The improved system is evaluated on the NIST LRE-2005 and the LRE-2007 evaluation data sets. Despite its simplicity, the system achieves for the 30s condition a Cavg of 2.4% and 1.6% on these data sets, respectively.