Abstract

The sensitivity of Automatic Speech Recognition (ASR) systems to the presence of background noises in the speaking environment, still remains a challenging task. Extracting noise robust features to compensate for speech degradations due to the noise, regained popularity in recent years. This paper contributes to this trend by proposing a cost-efficient denoising method that can serve as a preprocessing stage in any feature extraction scheme to boost its ASR performance. Recognition performance on Aurora2 shows that a noise robust frontend is obtained when combined with noise masking and feature normalization. Without the requirement of high computational costs, the method achieves similar recognition results when compared to other state-of-the art noise compensation methods.

Date

2013

Authors

Maarten Van Segbroeck, Shrikanth S Narayanan

Conference

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

Pages

7097-7101

Publisher

IEEE