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1. Овсянникова В. В. К вопросу о классификации эмоций: категориальный и многомерный подходы // Финансовая аналитика: проблемы и решения. – 2013. – №. 37. – С. 43-48.
2. Рогов Е. И. Настольная книга практического психолога: Учебное пособие // М.: ВЛАДОС. – 1998. – С. 134-142.
3. Самигулин Т. Р., Смирнов И. З., Лаушкина А. А. Определение маркеров агрессивного поведения человека на основе анализа аудио и текстового каналов // Научный результат. Информационные технологии. – 2022. – Т. 7. – №. 2. – С. 55-62.
4. Bazarevsky V. et al. Blazeface: Sub-millisecond neural face detection on mobile gpus //arXiv preprint arXiv:1907.05047. – 2019.
5. Burkhardt F. et al. A database of German emotional speech // Interspeech. – 2005. – Т. 5. – С. 1517-1520.
6. Chow A., Louie J. Detecting lies via speech patterns. – 2017.
7. Devyatkin D. A. et al. Intelligent analysis of manifestations of verbal aggressiveness in network community texts //Scientific and Technical Information Processing. – 2014. – Т. 41. – С. 377-389.
8. Goupil L. et al. Listeners’ perceptions of the certainty and honesty of a speaker are associated with a common prosodic signature // Nature communication. – 2021. – Т. 12. – №. 1. – С. 861.
9. Gournay P., Lahaie O., Lefebvre R. A canadian french emotional speech dataset //Proceedings of the 9th ACM multimedia systems conference. – 2018. – С. 399-402.
10. Korobov M. Morphological analyzer and generator for Russian and Ukrainian languages //Analysis of Images, Social Networks and Texts: 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers 4. – Springer International Publishing, 2015. – С. 320-332.
11. Kossaifi J. et al. Sewa db: A rich database for audio-visual emotion and sentiment research in the wild // IEEE transactions on pattern analysis and machine intelligence. – 2019. – Т. 43. – №. 3. – С. 1022-1040.
12. Laushkina, Anastasia & Smirnov, Ivan & Medvedev, Anatoly & Laptev, Andrey & Sinko, Mikhail. (2022). Detecting incongruity in the expression of emotions in short videos based on a multimodal approach. Cybernetics and Physics. 210-216.
13. Luna-Jiménez C. et al. A Proposal for Multimodal Emotion Recognition Using Aural Transformers and Action Units on RAVDESS Dataset // Applied Sciences. 2021. – Vol. 12. – № 1. – P. 327.
14. Marcolla F., de Santiago R., Dazzi R. Novel Lie Speech Classification by using Voice Stress // Proceedings of the 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS – Science and Technology Publications. – 2020. – С. 742–749.
15. McFee B. et al. librosa: Audio and music signal analysis in python //Proceedings of the 14th python in science conference. – 2015. – Т. 8. – С. 18-25.
16. Oviatt S. et al. (ed.). The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition-Volume 2. – Association for Computing Machinery and Morgan & Claypool, 2018.
17. S. Haq P.J.B.J. Multimodal Emotion Recognition / ed. Wang W. IGI Global, 2010. C. 398–423.
18. Saeed H. H., Shahzad K., Kamiran F. Overlapping toxic sentiment classification using deep neural architectures //2018 IEEE international conference on data mining workshops (ICDMW). – IEEE, 2018. – С. 1361-1366.
19. Sinko M. et al. Method of constructing and identifying predictive models of human behavior based on information models of non-verbal signals //Procedia Computer Science. – 2022. – Т. 212. – С. 171-180.
20. Tenney I., Das D., Pavlick E. BERT rediscovers the classical NLP pipeline // arXiv preprint arXiv:1905.05950. – 2019.
21. Tsai Y. H. H. et al. Learning factorized multimodal representations // arXiv preprint arXiv:1806.06176. – 2018.
22. Tsai Y. H. H. et al. Multimodal transformer for unaligned multimodal language sequences //Proceedings of the conference. Association for Computational Linguistics. Meeting. – NIH Public Access, 2019. – Т. 2019. – С. 6558.
23. Tzirakis P. et al. End-to-end multimodal emotion recognition using deep neural networks //IEEE Journal of selected topics in signal processing. – 2017. – Т. 11. – №. 8. – С. 1301-1309.
24. В МВФ оценили потери мировой экономике от коррупции [Электронный ресурс]. – Режим доступа: https://www.rbc.ru/economics/18/09/2017/59bfead89a794704d063c4f0 (дата обращения: 03.04.2023).
25. Краткая характеристика состояния преступности в Российской Федерации за январь-июнь 2022 года. URL: https://xn--b1aew.xn--p1ai/reports/item/31209853/ (дата обращения: 03.04.2023).
26. НИУ ВШЭ предложил использовать искусственный интеллект для предотвращения коррупции в РФ [Электронный ресурс]. – Режим доступа: https://tass.ru/ekonomika/14304599 (дата обращения: 03.04.2023).
27. Bert for Sequence Classification (Question vs Statement)  [Электронный ресурс]. – Режим доступа: https://sparknlp.org/2021/11/04/bert_sequence_classifier_question_statement_en.html (дата обращения: 15.04.2023).