Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition
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 Aurora-2 database, and 2.93 absolute improvements over Aurora-2 and Aurora-4 baseline models respectively. Run-time adaptation shows 9.89 compared to SPLICE for Test C, and 4.96 adaptation on HMMs.
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