Abstract
This paper proposes an automatic system which uses multimodal techniques for automatically estimating oral presentation skills. It is based on a set of features from three sources; audio, gesture and power-point slides. Machine learning techniques are used to classify each presentation into two classes (high vs. low) and into three classes; low, average, and high-quality presentation. Around 448 Multimodal recordings of the MLA’14 dataset were used for training and evaluating three different 2-class and 3-class classifiers. Classifiers were evaluated for each feature type independently and for all features combined together. The best accuracy of the 2-class systems is 90.1% achieved by SVM trained on audio features and 75% for 3-class systems achieved by random forest trained on slides features. Combining three feature types into one vector improves all systems accuracy by around 5%.
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Hanani, A., Al-Amleh, M., Bazbus, W., Salameh, S. (2017). Automatic Estimation of Presentation Skills Using Speech, Slides and Gestures. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_17
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DOI: https://doi.org/10.1007/978-3-319-66429-3_17
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