References

mais

Моделирование и анализ информационных систем

Modeling and Analysis of Information Systems

1818-10152313-5417

Yaroslavl State University

10.18255/1818-1015-2025-3-252-281

mais-1962

Research Article

Artificial Intelligence

Анализ мультимодальных данных в распознавании эмоций

Multimodal data analysis in emotion recognition: a review

https://orcid.org/0009-0006-4484-3853

Бердышев

Даниил Алексеевич

Berdyshev

Daniil A.

danberdyshev@gmail.com

https://orcid.org/0000-0002-6170-8630

Шишкин

Алексей Геннадиевич

Shishkin

Aleksei G.

shishkin@cs.msu.ru

Московский государственный университет им. М.В. ЛомоносоваРоссияLomonosov Moscow State UniversityRussian Federation

2025

24092025

323252281

2025

Бердышев Д.А., Шишкин А.Г.

Berdyshev D.A., Shishkin A.G.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://www.mais-journal.ru/jour/article/view/1962

Использование мультимодальных данных в системах распознавания эмоций имеет огромный потенциал для приложений в различных областях: здравоохранение, человеко-машинные интерфейсы, контроль состояния операторов, маркетинг. До недавнего времени развитие систем распознавания эмоций на основе мультимодальных данных сдерживалось недостаточной мощностью вычислительной техники. Однако с появлением высокопроизводительных систем на основе графических процессоров и разработкой эффективных архитектур глубоких нейронных сетей произошел всплеск исследований, направленных на использование нескольких модальностей, таких как аудио, видео и физиологические сигналы, для точного определения человеческих эмоций. Помимо этого, немаловажную роль стали играть физиологические данные, полученные с помощью носимых устройств, благодаря относительной простоте их сбора и точности, которую они позволяют достигать. В данной статье рассмотрены архитектуры и методы применения глубоких нейронных сетей для анализа мультимодальных данных с целью повышения точности и надежности систем распознавания эмоций, представлены современные подходы к реализации таких алгоритмов и существующие открытые наборы мультимодальных данных.

The use of multimodal data in emotion recognition systems has great potential for applications in various fields: healthcare, human-machine interfaces, operator monitoring, and marketing. Until recently, the development of emotion recognition systems based on multimodal data was constrained by insufficient computing power. However, with the advent of high-performance GPU-based systems and the development of efficient deep neural network architectures, there has been a surge of research aimed at using multiple modalities such as audio, video, and physiological signals to accurately detect human emotions. In addition, physiological data from wearable devices has become important due to the relative ease of its collection and the accuracy it enables. This paper discusses architectures and methods for applying deep neural networks to analyse multimodal data to improve the accuracy and reliability of emotion recognition systems, presenting current approaches to implementing such algorithms and existing open multimodal datasets.

распознавание эмоциймультимодальные данныенейронные сетимашинное обучение

emotion recognitionmultimodal dataneural networksmachine learning

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The authors declare that there are no conflicts of interest present.