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The paper is to improve Automatic Speech Recognition (ASR) performance in very noisy and reverberant environments. The solution is based on extracting sub-band spectral variance normalization based features, which are capable of estimating the relative strengths of speech and noise components both in presence and absence of speech. The advanced ETSI-2 frontend, RASTA-PLP, MFCC alone and in combination with spectral subtraction are tested for comparison purposes. Speech recognition evaluations are performed on the noisy standard AURORA-2 and Meeting Recorder Digit (MRD) subset of AURORA-5 databases, which represent additive noise and reverberant acoustic conditions.
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