| Abstract |
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We propose a novel real-time affect classification system based on features extracted from the acoustic speech signal. The proposed system analyses the speech signal and provides a real-time classification of the speaker’s perceived affective state. A neural network is trained and tested using a database of 391 authentic emotional utterances from 11 speakers. Two emotions, anger and neutral, are considered. The system is designed to be speaker and text-independent and is to be deployed in a call-centre environment to assist in the handling of customer inquiries. We achieve a success rate of 80.1% accuracy in our preliminary results.
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 Additional Information
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Citation:
Donn Morrison, Ruili Wang, Liyanage C. De Silva, W. L. Xu,
"Real-Time Spoken Affect Classification and Its Application in Call-Centres,"
icita,
pp. 483-487,
Third International Conference on Information Technology and Applications (ICITA'05) Volume 1,
2005
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