Fundamentals of speech recognition
Fundamentals of speech recognition
The nature of statistical learning theory
The nature of statistical learning theory
Affective computing
Recognition of Affective Communicative Intent in Robot-Directed Speech
Autonomous Robots
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Emotion Detection from Speech to Enrich Multimedia Content
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
How to find trouble in communication
Speech Communication - Special issue on speech and emotion
Real-Time Spoken Affect Classification and Its Application in Call-Centres
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
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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.