Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Ensemble majority voting classifier for speech emotion recognition and prediction

Ensemble majority voting classifier for speech emotion recognition and prediction Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one‐against‐all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Systems and Information Technology Emerald Publishing

Ensemble majority voting classifier for speech emotion recognition and prediction

Download PDF
11 pages

Loading next page...
 
/lp/emerald-publishing/ensemble-majority-voting-classifier-for-speech-emotion-recognition-and-TeuemQs070
Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
1328-7265
DOI
10.1108/JSIT-01-2014-0009
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one‐against‐all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.

Journal

Journal of Systems and Information Technology – Emerald Publishing

Published: Aug 5, 2014

Keywords: Speech emotion recognition; Affective computing; Machine learning

References

  • Introduction to Machine Learning
    Alpaydin, E.
  • Class‐level spectral features for emotion recognition
    Bitouk, D.; Verma, R.; Nenkova, A.
  • The HUMAINE database: addressing the collection and annotation of naturalistic and induced emotional data
    Douglas‐Cowie, E.; Cowie, R.; Sneddon, I.; Cox, C.; Lowry, O.; McRorie, M.; Martin, J.‐C.; Devillers, L.; Abrilian, S.; Batliner, A.; Amir, N.; Karpouzis, K.
  • Pattern Classification
    Duda, R.O.; Hart, P.E.; Stork, D.G.
  • An argument for basic emotions
    Ekman, P.
  • Evolutionary Psychology
    Gaulin, S.J.C.; McBurney, D.H.
  • Multiple kernel learning algorithms
    Gonen, M.; Alpaydin, E.
  • Combining categorical and primitives‐based emotion recognition
    Grimm, M.; Mower, E.; Kroschel, K.; Narayanan, S.
  • The Elements of Statistical Learning
    Hastie, T.; Tibshirani, R.; Friedman, J.
  • Theory and Application of the Z‐Transform Method
    Jury, E.I.
  • Real‐time emotion detection system using speech: multi‐modal fusion of different timescale features
    Kim, S.
  • Emotional Intelligence Science and Myth
    Matthews, G.; Zeidner, M.; Roberts, R.D.
  • Combined speech‐emotion recognition for spoken human‐computer interfaces
    Meng, H.; Pittermann, J.; Pittermann, A.; Minker, W.
  • Worry spreads: interpersonal transfer of problem‐related anxiety
    Parkinson, B.; Gwenda, S.
  • Affective Computing
    Picard, R.W.
  • Acoustic features extraction for emotion recognition
    Rong, J.; Chen, Y.‐P.P.; Chowdhury, M.; Gang, L.
  • Emotion recognition using mel‐frequency cepstral coefficients
    Sato, N.; Obuchi, Y.
  • Psychology
    Schacter, D.L.
  • Vocal communication of emotion: a review of research paradigms
    Scherer, K.R.
  • Phonetic and speaker variations in automatic emotion classification
    Sethu, V.; Ambikairaja, E.; Epps, J.
  • Predictive Inference
    Seymour, G.
  • Segment‐based approach to the recognition of emotions in speech
    Shamia, M.; Kamel, M.
  • Selecting and interpreting measures of thematic classification accuracy
    Stehman, S.V.
  • Development and validation of an internationally reliable short‐form of the positive and negative affect schedule (PANAS)
    Thompson, E.R.
  • The Nature of Statistical Learning Theory
    Vapnik, V.N.
  • Frame vs turn‐level: emotion recognition from speech considering static and dynamic processing
    Vlasenko, B.; Schuller, B.; Wendemuth, A.; Rigoll, G.
  • Recognition of emotions in german speech using gaussian mixture models
    Vondra, M.; Vich, R.
  • Speech‐based emotion classification using multiclass SVM with hybrid kernel and thresholding fusion
    Yang, N.; Muraleedharan, R.; Kohl, J.; Demirkol, I.; Heinzelman, W.; Sturge‐Apple, M.
  • Speech emotion classification on a riemannian manifold
    Ye, C.; Liu, J.; Chen, C.; Song, M.; Bu, J.