@inproceedings{tashev2006suppression,
author = {Tashev, Ivan and Droppo, Jasha and Acero, Alex},
title = {Suppression Rule for Speech Recognition Friendly Noise Suppressors},
booktitle = {Proceedings of Eight International Conference Digital Signal Processing and Applications DSPA'06},
year = {2006},
month = {March},
abstract = {Audio signal enhancement often involves the application of a time-varying filter, or suppression rule, to the frequency-domain transform of a corrupted signal. Known approaches use rules derived under Gaussian models and interpret them as spectral estimators in a Bayesian statistical framework. While this mathematical approach provides rules that satisfy  certain optimization criteria these rules are not optimal when the enhanced signal is for a speech recognition engine. In this paper we present the approach and the results for creation of a speech recognition friendly suppression rule. The described approach increases the average speech recognition rate in Aurora 2 tests from 52.47% to 77.69% while maintaining performance for low noise utterances.},
url = {http://approjects.co.za/?big=en-us/research/publication/suppression-rule-for-speech-recognition-friendly-noise-suppressors/},
}