@inproceedings{giannoulis-potamianos-2012-hierarchical,
title = "A hierarchical approach with feature selection for emotion recognition from speech",
author = "Giannoulis, Panagiotis and
Potamianos, Gerasimos",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/917_Paper.pdf",
pages = "1203--1206",
abstract = "We examine speaker independent emotion classification from speech, reporting experiments on the Berlin database across six basic emotions. Our approach is novel in a number of ways: First, it is hierarchical, motivated by our belief that the most suitable feature set for classification is different for each pair of emotions. Further, it uses a large number of feature sets of different types, such as prosodic, spectral, glottal flow based, and AM-FM ones. Finally, it employs a two-stage feature selection strategy to achieve discriminative dimensionality reduction. The approach results to a classification rate of 85{\%}, comparable to the state-of-the-art on this dataset.",
}
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%0 Conference Proceedings
%T A hierarchical approach with feature selection for emotion recognition from speech
%A Giannoulis, Panagiotis
%A Potamianos, Gerasimos
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F giannoulis-potamianos-2012-hierarchical
%X We examine speaker independent emotion classification from speech, reporting experiments on the Berlin database across six basic emotions. Our approach is novel in a number of ways: First, it is hierarchical, motivated by our belief that the most suitable feature set for classification is different for each pair of emotions. Further, it uses a large number of feature sets of different types, such as prosodic, spectral, glottal flow based, and AM-FM ones. Finally, it employs a two-stage feature selection strategy to achieve discriminative dimensionality reduction. The approach results to a classification rate of 85%, comparable to the state-of-the-art on this dataset.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/917_Paper.pdf
%P 1203-1206
Markdown (Informal)
[A hierarchical approach with feature selection for emotion recognition from speech](http://www.lrec-conf.org/proceedings/lrec2012/pdf/917_Paper.pdf) (Giannoulis & Potamianos, LREC 2012)
ACL