In this section
@inproceedings{Papayiannis:2017:10.1109/ICASSP.2017.7952257,
author = {Papayiannis, C and Evers, C and Naylor, PA},
doi = {10.1109/ICASSP.2017.7952257},
publisher = {IEEE},
title = {Discriminative feature domains for reverberant acoustic environments},
url = {http://dx.doi.org/10.1109/ICASSP.2017.7952257},
year = {2017}
}
TY - CPAPER
AB - Several speech processing and audio data-mining applicationsrely on a description of the acoustic environment as a featurevector for classification. The discriminative properties of thefeature domain play a crucial role in the effectiveness of thesemethods. In this work, we consider three environment iden-tification tasks and the task of acoustic model selection forspeech recognition. A set of acoustic parameters and Ma-chine Learning algorithms for feature selection are used andan analysis is performed on the resulting feature domains foreach task. In our experiments, a classification accuracy of100% is achieved for the majority of tasks and the Word Er-ror Rate is reduced by 20.73 percentage points for AutomaticSpeech Recognition when using the resulting domains. Ex-perimental results indicate a significant dissimilarity in theparameter choices for the composition of the domains, whichhighlights the importance of the feature selection process forindividual applications.
AU - Papayiannis,C
AU - Evers,C
AU - Naylor,PA
DO - 10.1109/ICASSP.2017.7952257
PB - IEEE
PY - 2017///
SN - 2379-190X
TI - Discriminative feature domains for reverberant acoustic environments
UR - http://dx.doi.org/10.1109/ICASSP.2017.7952257
UR - http://hdl.handle.net/10044/1/43617
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom