The paper describes an auditory processing-based feature extraction strategy for robust speech recognition in\nenvironments, where conventional automatic speech recognition (ASR) approaches are not successful. It incorporates\na combination of gammatone filtering, modulation spectrum and non-linearity for feature extraction in the\nrecognition chain to improve robustness, more specifically the ASR in adverse acoustic conditions. The experimental\nresults with standard Aurora-4 large vocabulary evaluation task revealed that the proposed features provide reliable\nand considerable improvement in terms of robustness in different noise conditions and are comparable to those of\nstandard feature extraction techniques.
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