Abstract
This paper reports a study of automatic attitude recognition from a collection of over 500 segments of our video blog data. We annotated and analysed 3 different attitudinal states of the speakers. Following that, we extracted and analysed prosodic and visual features relevant to the classification task. We use machine learning methods and techniques to attain better understanding of the feature sets and their contribution to the prediction model.
Keywords
- Attitude prediction
- Audio and visual feature analysis
- Machine learning
- Random forest
- LibSVM
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Notes
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https://www.youtube.com/user/nigahiga, https://www.youtube.com/user/JustinJamesHughes, https://www.youtube.com/user/uncuthashbrown, https://www.youtube.com/user/kevjumba, https://www.youtube.com/user/tyleroakley, https://www.youtube.com/user/ConnorFranta, https://www.youtube.com/user/TimothyDeLaGhetto2, https://www.youtube.com/user/DavidSoComedy, https://www.youtube.com/user/michaelbalalis, https://www.youtube.com/user/shane.
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Acknowledgments
This work is supported by the English Language and Literature Department, UPSI, Ministry of Education Malaysia, Center for Global Intelligent Content (CNGL) at TCD and the Speech Communication Laboratory at TCD.
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© 2015 Springer International Publishing Switzerland
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Madzlan, N.A., Huang, Y., Campbell, N. (2015). Automatic Classification and Prediction of Attitudes: Audio - Visual Analysis of Video Blogs. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_12
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DOI: https://doi.org/10.1007/978-3-319-23132-7_12
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