• Voting ensembles for spoken af...

Treffer: Voting ensembles for spoken affect classification : Information technology

Title:
Voting ensembles for spoken affect classification : Information technology
Authors:
MORRISON, Donn, DE SILVA, Liyanage C
Source:
Journal of network and computer applications. 30(4):1356-1365
Publisher Information:
London: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 1 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Electronique, Electronics, Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Réseaux neuronaux, Neural networks, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Traitement du signal, Signal processing, Traitement de la parole, Speech processing, Arbre décision, Decision tree, Arbol decisión, Base donnée, Database, Base dato, Classification automatique, Automatic classification, Clasificación automática, Classification signal, Signal classification, Evaluation performance, Performance evaluation, Evaluación prestación, Machine vecteur support, Support vector machine, Máquina vector soporte, Perceptron multicouche, Multilayer perceptrons, Plus proche voisin, Nearest neighbour, Vecino más cercano, Prosodie, Prosody, Prosodia, Précision, Accuracy, Precisión, Reconnaissance émotion, Emotion recognition, Traitement parole, Speech processing, Tratamiento palabra, Vote, Voting, Voto, Réseau neuronal, Affect recognition, Ensemble methods
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Information Sciences and Technology, Massey University, Private bag 11222, Palmerston North, New Zealand
ISSN:
1084-8045
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19102948
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Electronics

Telecommunications and information theory
Accession Number:
edscal.19102948
Database:
PASCAL Archive

Weitere Informationen

Affect or emotion classification from speech has much to benefit from ensemble classification methods. In this paper we apply a simple voting mechanism to an ensemble of classifiers and attain a modest performance increase compared to the individual classifiers. A natural emotional speech database was compiled from 11 speakers. Listener-judges were used to validate the emotional content of the speech. Thirty-eight prosody-based features correlating characteristics of speech with emotional states were extracted from the data. A classifier ensemble was designed using a multi-layer perceptron, support vector machine, K* instance-based learner, K-nearest neighbour, and random forest of decision trees. A simple voting scheme determined the most popular prediction. The accuracy of the ensemble is compared with the accuracies of the individual classifiers.