Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/74414
Type: Artigo de periódico
Title: Spoken emotion recognition through optimum-path forest classification using glottal features
Author: Iliev, AI
Scordilis, MS
Papa, JP
Falcao, AX
Abstract: A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPE generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs. (C) 2009 Elsevier Ltd. All rights reserved.
Subject: Emotion recognition
Glottal analysis
Speech analysis
Optimum-path forest
Country: Inglaterra
Editor: Academic Press Ltd- Elsevier Science Ltd
Rights: fechado
Identifier DOI: 10.1016/j.csl.2009.02.005
Date Issue: 2010
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

Files in This Item:
File Description SizeFormat 
WOS000277330400003.pdf1.06 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.