Title: Incremental learning for spoken affect classification and its application in call-centres

Authors: Donn Morrison, Ruili Wang, W.L. Xu, Liyanage C. De Silva

Addresses: Institute of Information Sciences and Technology, Massey University, Private Bag 11222, Palmerston North, New Zealand. ' Institute of Information Sciences and Technology, Massey University, Private Bag 11222, Palmerston North, New Zealand. ' Institute of Technology and Engineering, Massey University, Private Bag 11222, Palmerston North, New Zealand. ' Institute of Information Sciences and Technology, Massey University, Private Bag 11222, Palmerston North, New Zealand

Abstract: This paper introduces a system for real-time incremental learning in a call-centre environment. The classifier used is a Support Vector Machine (SVM) and it is applied to telephone-based spoken affect classification. A database of 391 natural speech samples depicting angry and neutral speech is collected from 11 speakers. Using this data and features shown to correlate speech with emotional states, a SVM-based classification model is trained. Forward selection is employed on the feature space in an attempt to prune redundant or harmful dimensions. The resulting model offers a mean classification rate of 88.45% for the two-class problem. Results are compared with those from an Artificial Neural Network (ANN) designed under the same circumstances.

Keywords: incremental learning; real-time; affect recognition; emotion recognition; spoken affect classification; support vector machines; SVMs; artificial neural networks; ANNs; automatic learning; call centres.

DOI: 10.1504/IJISTA.2007.012486

International Journal of Intelligent Systems Technologies and Applications, 2007 Vol.2 No.2/3, pp.242 - 254

Available online: 19 Feb 2007 *

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