Author
Bozkurt, E., Erzin, E., Erdem, Tanju, Eroğlu Erdem, Ç.
Publication Date
2011
Publication Place
-
Springer International Publishing
Subject
Affect recognition, Emotional speech classification, RANSAC, Data cleaning, Decision fusion
Type
Book
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
0302-9743
Record ID
15a4496b-b117-4f1e-8eb7-a0d2157fe788
Library Location
Computer Science
Date
2011
Notes
TÜBİTAK
Sample Text
Training datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequency (LSF) features indicate that utilization of RANSAC in the training phase provides an improvement in the unweighted recall rates on the test set. Experimental studies performed over the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF and MFCC based classifiers provide further significant performance improvements.
DOI
10.1007/978-3-642-25775-9_3
Cilt
6800
