In this paper we report on recent improvements in the University of Colorado system for the DARPA/NRL Speech in Noisy Environments (SPINE) task. In particular, we describe our efforts on improving acoustic and language modeling for the task and investigate methods for unsupervised speaker and environment adaptation from limited data. We show that the MAPLR adaptation method outperforms single and multiple regression class MLLR on the SPINE task. Our current SPINE system uses the Sonic speech recognition engine that was recently developed at the University of Colorado. This system is shown to have a word error rate of 31.5% on the SPINE-2 evaluation data. These improvements amount to a 16% reduction in relative word error rate compared to our previous SPINE-2 system fielded in the Nov. 2001 DARPA/NRL evaluation.
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