We aim to provide an automatic anti-bullying component in online text and speech based interaction for young people age 18 - 25. Affect expression in speech generally differs from culture to culture, from female to male. In this study, we focus on affect sensing from speech for different gender user groups for young people. So far our work mainly concentrates on the sensing of five basic emotions (including 'happiness', 'sadness', 'fear', 'surprise', and 'anger') and 'neutral' from speech. Detailed acoustic features have been extracted after analysis of speech data from one chosen male and female speaker. Our affect sensing component has been implemented under the theory of naïve Bayes classifier. We have also evaluated it using new test data. Our work contributes to the conference themes on intelligent technologies - machine learning and affective speech processing.
|Title of host publication||Artificial intelligence in education - Building learning systems that care: From knowledge representation to affective modelling|
|Pages||683 - 685|
|Publication status||Published - 2009|