In this paper, a modification to the training process of the popular SPLICE
algorithm has been proposed for noise robust speech recognition. The
modification is based on feature correlations, and enables this stereo-based
algorithm to improve the performance in all noise conditions, especially in
unseen cases. Further, the modified framework is extended to work for
non-stereo datasets where clean and noisy training utterances, but not stereo
counterparts, are required. Finally, an MLLR-based computationally efficient
run-time noise adaptation method in SPLICE framework has been proposed. The
modified SPLICE shows 8.6% absolute improvement over SPLICE in Test C of
Aurora-2 database, and 2.93% overall. Non-stereo method shows 10.37% and 6.93%
absolute improvements over Aurora-2 and Aurora-4 baseline models respectively.
Run-time adaptation shows 9.89% absolute improvement in modified framework as
compared to SPLICE for Test C, and 4.96% overall w.r.t. standard MLLR
adaptation on HMMs.Comment: Submitted to Automatic Speech Recognition and Understanding (ASRU)
2013 Worksho