Comparative evaluation of single-channel MMSE-Based noise reduction schemes for speech recognition

Authors:
Emanuele Principi;Simone Cifani;Rudy Rotili;Stefano Squartini;Francesco Piazza
Affiliations:
3MediaLabs, Department of Biomedical Engineering, Electronics and Telecommunications, Polytechnic University of Marche, Ancona, Italy;3MediaLabs, Department of Biomedical Engineering, Electronics and Telecommunications, Polytechnic University of Marche, Ancona, Italy;3MediaLabs, Department of Biomedical Engineering, Electronics and Telecommunications, Polytechnic University of Marche, Ancona, Italy;3MediaLabs, Department of Biomedical Engineering, Electronics and Telecommunications, Polytechnic University of Marche, Ancona, Italy;3MediaLabs, Department of Biomedical Engineering, Electronics and Telecommunications, Polytechnic University of Marche, Ancona, Italy
Venue:
Journal of Electrical and Computer Engineering
Year:
2010

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Abstract

One of the big challenges in the field of Automatic Speech Recognition (ASR) consists in developing suitable solutions able to work properly also in adverse acoustic conditions, like in presence of additive noise and/or in reverberant rooms. Recently a certain attention has been paid to deeply integrate the noise suppressor in the feature extraction pipeline. In this paper, different single-channel MMSE-based noise reduction schemes have been implemented both in the frequency and cepstral domains and the related recognition performances evaluated on the AURORA2 and AURORA4 databases, therefore providing a useful reference for the scientific community.