Video blogging is a form of unidirectional communication where a video blogger expresses his/her opinion about different issues. The success of a video blog is measured using metrics like the number of views and comments by online viewers. Researchers have highlighted the importance of non-verbal behaviours (e.g. attitudes) in the context of video blogging and showed that it correlates with the level of attention (number of views) gained by a video blog. Therefore, an automatic attitude recognition system can help potential video bloggers to train their attitudes. It can also be useful in developing video blogs summarization and search tools. This study proposes a novel Active Feature Transformation (AFT) method for automatic recognition of attitudes (a form of non-verbal behaviour) in video blogs. The proposed method transforms the Mel-frequency Cepstral Coefficient (MFCC) features for the classification task. The Principal Component Analysis (PCA) transformation is also used for comparison. Our results show that AFT outperforms PCA in terms of accuracy and dimensionality reduction for attitude recognition using linear discrimination analysis, 1-nearest neighbour and decision tree classifiers.
Cite as: Haider, F., A. Salim, F., Conlan, O., Luz, S. (2018) An Active Feature Transformation Method for Attitude Recognition of Video Bloggers. Proc. Interspeech 2018, 431-435, doi: 10.21437/Interspeech.2018-1222
@inproceedings{haider18_interspeech,
author={Fasih Haider and Fahim {A. Salim} and Owen Conlan and Saturnino Luz},
title={{An Active Feature Transformation Method for Attitude Recognition of Video Bloggers}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={431--435},
doi={10.21437/Interspeech.2018-1222}
}