Abstract
This paper presents the results of experiments on applying ensemble learning techniques and neural networks to a paralinguistic analysis of deceptive and truthful statements in the flow of speech. Based on an analysis and comparison of different approaches to the issue, we propose using a mixture of such methods. The Real-Life Trial Deception Detection Dataset was used for both training and testing. All the experiments were performed using 10-fold cross-validation. Using two-layer neural networks, k-nearest neighbor, random forest for evaluating and principal component analysis methods for preprocessing, results in UAR of 65.0% and 70.0%, in the case of average and majority voting correspondingly.
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This research is supported by the Russian Science Foundation (project No. 18-11-00145).
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Velichko, A., Budkov, V., Kagirov, I., Karpov, A. (2020). Applying Ensemble Learning Techniques and Neural Networks to Deceptive and Truthful Information Detection Task in the Flow of Speech. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_56
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