Comparative Analysis of Classification Methods for Automatic Deception Detection in Speech

Velichko, Alena; Budkov, Victor; Kagirov, Ildar; Karpov, Alexey

doi:10.1007/978-3-319-99579-3_75

Cited by 4 publications

(5 citation statements)

References 21 publications

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“…Decision Trees appeared 21 times (18.58%), among which 19 [ 40 , 43 , 45 , 49 , 58 , 60 , 71 , 73 , 86 , 96 – 98 , 103 , 105 , 107 , 108 , 113 ] in its vanilla flavor (the technique as originally proposed). Those measured performance by accuracy, which ranges from 0.5708 to 0.9800, with a mean at 0.5708 ± 0.1370.…”

Section: Discussionmentioning

confidence: 99%

“…Statistical analysis reveals that all those textual Monomodal approaches trained their classifiers with data extracted from non-real-life situations and online deception game sessions. Only a few visual and vocal Monomodal studies built their classifiers from real-life data [60,73,77].…”

Section: Complexity and Performancementioning

confidence: 99%

“…Among the 20 studies that exploited Random Forest, 16 [40,48,56,60,66,73,75,77,78,84,86,89,92,98,105,114] measured their performance by accuracy, which ranges from 0.5677 to 0.9980, with mean at 0.7301 ± 0.1182. Three other studies [52,57,87] measured their performance by Area Under the Curve, which values 0.7330, 0.8131, and 0.8970.…”

Section: Artificial Neuralmentioning

confidence: 99%

“…KNN appeared 10 times (8.85%), among which eight [43,60,61,67,72,73,80,98] in its vanilla flavor. Those measured performance by accuracy, which ranges from 0.5723 to 0.8688, with a mean at 0.7527 ± 0.1029.…”

Section: Artificial Neuralmentioning

confidence: 99%

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Deception detection with machine learning: A systematic review and statistical analysis

et al. 2023

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Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for contribution. The present literature review aims to provide the state of research regarding deception detection with Machine Learning. We followed the PRISMA protocol and retrieved 648 articles from ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. 540 of them were screened (108 were duplicates). A final corpus of 81 documents has been summarized as mind maps. Metadata was extracted and has been encoded as Python dictionaries to support a statistical analysis scripted in Python programming language, and available as a collection of Jupyter Lab Notebooks in a GitHub repository. All are available as Jupyter Lab Notebooks. Neural Networks, Support Vector Machines, Random Forest, Decision Tree and K-nearest Neighbor are the five most explored techniques. The studies report a detection performance ranging from 51% to 100%, with 19 works reaching accuracy rate above 0.9. Monomodal, Bimodal, and Multimodal approaches were exploited and achieved various accuracy levels for detection. Bimodal and Multimodal approaches have become a trend over Monomodal ones, although there are high-performance examples of the latter. Studies that exploit language and linguistic features, 75% are dedicated to English. The findings include observations of the following: language and culture, emotional features, psychological traits, cognitive load, facial cues, complexity, performance, and Machine Learning topics. We also present a dataset benchmark. Main conclusions are that labeled datasets from real-life data are scarce. Also, there is still room for new approaches for deception detection with Machine Learning, especially if focused on languages and cultures other than English-based. Further research would greatly contribute by providing new labeled and multimodal datasets for deception detection, both for English and other languages.

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Section: Discussionmentioning

confidence: 99%

Section: Complexity and Performancementioning

confidence: 99%

Section: Artificial Neuralmentioning

confidence: 99%

Section: Artificial Neuralmentioning

confidence: 99%

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Deception detection with machine learning: A systematic review and statistical analysis

et al. 2023

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“…In Enos's Ph.D. thesis, he conducted a comparative analysis of the performance of fve algorithms, including support vector machines, naive Bayes, logistic regression, decision trees, and ripper algorithms; the results showed that decision trees and support vector machines have better performance [9]. Velichko et al analysed many diferent machine learning algorithms on the real-life trail dataset, and the most effective random forest algorithm achieved an accuracy of 79.4% [10]. Te literature [11] investigated the impact of ensemble learning methods on deception detection performance, achieving a 70% recognition rate with the real-life trail dataset.…”

Section: Introductionmentioning

confidence: 99%

Speech Deception Detection Based on EMD and Temporal Neural Network

Jiang

Chen

Yuan

et al. 2023

Computational Intelligence and Neuroscience

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Deceptive behaviour is a common phenomenon in human society. Research has shown that humans are not good at distinguishing deception, so studying automated deception detection techniques is a critical task. Most of the relevant technologies are susceptible to personal and environmental influences: EEG-based technologies need large and expensive equipment, facial-based technologies are sensitive with the camera’s perspective, and these reasons have somewhat limited the development of applications for deception detection technologies. In contrast, the equipment required for speech deception detection is cheap and easy to use, and the capture of speech is highly covert. Based on the application of signal decomposition algorithms in other fields such as EEG signals and speech emotion recognition, this paper proposed a signal decomposition and reconstruction method based on EMD to process the speech signal and a better deception detection performance was obtained by improving the speech quality. The comparison results with other decomposition algorithms showed that the EMD decomposition algorithm is the most suitable for our method. Across many different classification algorithms, accuracy improved by an average of 2.05% and the F1 score improved by an average of 1.7%. In addition, a new deception detector, called the TCN-LSTM network, was proposed in this paper. Experiments showed that this network organically combines the processing capability of TCN and LSTM for time series data; the recognition rate of deception detection was greatly improved, with the highest accuracy and F1 score reaching 86.2% and 86.0% under the EMD-based signal decomposition reconstruction method. Based on the research in this paper, the signal decomposition algorithms need to be further optimised for speech signals and more classification algorithms not used for this task should be tried.

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Applying Ensemble Learning Techniques and Neural Networks to Deceptive and Truthful Information Detection Task in the Flow of Speech

Velichko

Budkov

Kagirov

et al. 2019

Studies in Computational Intelligence

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Comparative Analysis of Classification Methods for Automatic Deception Detection in Speech

Cited by 4 publications

References 21 publications

Deception detection with machine learning: A systematic review and statistical analysis

Deception detection with machine learning: A systematic review and statistical analysis

Speech Deception Detection Based on EMD and Temporal Neural Network

Applying Ensemble Learning Techniques and Neural Networks to Deceptive and Truthful Information Detection Task in the Flow of Speech

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