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Универсальный и специфичный компоненты способности распознавания эмоциональной окраски информации
С. 27–34.
Permogorskiy M. S., Lyusin D.
Studies of emotion recognition ability do not give an exact answer to the question whether it is a general ability of a human being. We make an attempt to cover this gap using three types of stimuli: human behavior, music, non-musical sound stimuli. Two aspects of emotion recognition ability are proposed to be distinguished. One aspect is supposed to be accuracy of emotion modality recognition, i.e. an ability to recognize correctly modality of emotional state. Another aspect might be emotion sensitivity, i.e. a bias in emotion perception such that intensity of emotions is ‘overperceived’ or 'underperceived'. The hypothesis that sensitivity is a general component and accuracy is a specific component of emotion recognition ability was partly confirmed.
Keywords: emotional intelligenceраспознавание эмоцийemotion recognition abilityVideotest of Emotion Recognition
Publication based on the results of:
Zheltyakova M., Korotkov A., Bursov K. et al., Herald of the Russian Academy of Sciences 2026 Vol. 95 No. 6 P. 1048–1057
The Reading the Mind in the Eyes Test (RMET) is a widely used tool for assessing affective mentalization in both clinical and research contexts. Although translated into many languages, no validated Russian-language version has been available. Furthermore, the limited number of stimuli in the original RMET constrains its applicability in neuroimaging studies. This study presents ...
Added: September 13, 2026
Сысоева И. А., Sysoeva T., Новые психологические исследования 2026 № 3 С. 95–114
Currently, instant text messaging is widespread and often replaces regular offline communication. At the same time, such an important component of interaction as understanding the emotions of the interlocutor in online text communication may be difficult because of the lack of nonverbal emotional cues (facial expressions, gestures, etc.). Although it is generally believed that emotional ...
Added: June 22, 2026
Deeb B., Andrey V. Savchenko, Makarov I., IEEE Access 2026 Vol. 13 P. 56283–56295
Speech Emotion Recognition has gained considerable attention in speech processing and machine learning due to its potential applications in human-computer interaction, mental health monitoring, and customer service. However, state-of-the-art models for speech emotion recognition use many parameters, which leads to computational complexity. In this paper, we introduce a novel deep-learning model to enhance the accuracy ...
Added: June 16, 2026
Dvoynikova A., Кагиров И. А., Карпов А. А., Информатика и автоматизация (Труды СПИИРАН) 2024
В статье рассматривается проблема распознавания сентимента и эмоций пользователей в русскоязычных текстовых транскрипциях речи с использованием словарных методов и машинного перевода. Количество имеющихся информационных ресурсов для анализа сентимента текстовых сообщений на русском языке очень ограничено, что существенно затрудняет применение базовых методов анализа сентимента, а именно, предобработки текстов, векторизации с помощью тональных словарей, традиционных классификаторов. Для ...
Added: April 25, 2026
Dvoynikova A., В кн.: Альманах научных работ молодых ученых Университета ИТМО.: Университет ИТМО, 2023.
В статье раскрываются достоинства и недостатки категориальных и пространственных моделей описания эмоций. Пространственные модели позволяют охватить более широкий спектр человеческих эмоций, что позволяет разработать наиболее эффективную систему распознавания эмоций. В работе проводится аналитический обзор существующих многомодальных корпусов данных, которые имеют разметку по валентности и интенсивности эмоций. В заключении выделяется наиболее репрезентативный корпус данных для автоматического ...
Added: April 25, 2026
Dvoynikova A., Кондратенко К. О., Известия высших учебных заведений. Приборостроение 2023 Т. 66 № 10 С. 818–827
Аннотация. Исследован актуальный в различных областях вопрос распознавания эмоций в транскрипциях речи. Проанализировано влияние методов предобработки (удаление стоп-слов, лемматизация, стемминг) на точность распознавания эмоций в текстовых данных на русском и английском языках. Для проведения экспериментальных исследований использовались орфографические транскрипции диалогов из многомодальных корпусов RAMAS и CMU-MOSEI на русском и английском языке соответственно. Аннотирование этих корпусов ...
Added: April 25, 2026
Dvoynikova A., Мамонтов Д. Ю., Карпов А. А., В кн.: Альманах научных работ молодых ученых Университета ИТМОТ. 3.: Университет ИТМО, 2021. С. 63–68.
В работе проводятся экспериментальные исследования по определению уровня эмоциональных проявлений в текстовых транскрипциях базы данных K-EmoCon. Рассматривается влияние сбалансирования классов при обучении классификаторов на точность определения эмоций. В статье устанавливается базовый стандарт результатов по классификации уровня эмоций дикторов в текстовых транскрипциях. ...
Added: April 24, 2026
Kornilova T. V., Chumakova M., Maksarova L. B., Psychology in Russia: State of the Art 2025 Vol. 18 No. 1 P. 60–82
Background. This study is based on the idea of unity between affect and intelligence. It explores how different types of intelligence (fluid, verbal, self-assessed, and emotional) contribute to emotional creativity and implicit theories of creativity and emotion.
Objective. To identify the contributions of various types of intelligence to emotional creativity and the implicit theories of creativity and ...
Added: August 18, 2025
Deeb B., Savchenko A., Makarov I., , in: ECAI 2024. 27th European Conference on Artificial Intelligence, October 19 – 24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024).: IOS Press, 2024. P. 4479–4482.
In this paper, we introduce a novel tool for speech emotion recognition, CA-SER, that borrows self-supervised learning to extract semantic speech representations from a pre-trained wav2vec 2.0 model and combine them with spectral audio features to improve speech emotion recognition. Our approach involves a self-attention encoder on MFCC features to capture meaningful patterns in audio ...
Added: February 15, 2025
Petrakova A., Anikudimova E., Лебедева Е. И., В кн.: Лицо человека в системах коммуникации.: М.: Московский институт психоанализа, 2024. Гл. 10 С. 138–147.
Added: January 7, 2025
Petrakova A., Лебедева Е. И., Kuzmina Y. et al., Психология. Журнал Высшей школы экономики 2024 Т. 21 № 2 С. 423–431
This article presents a pilot study with the objective to create and test stimulus material, which consists of photographic portraits of adults and children expressing various emotions. The uniqueness of this work is due to the approach to organizing the creation of stimulus material, in which the models demonstrated emotions not according to an established ...
Added: December 26, 2024
Petrakova A., Лебедева Е. И., Anikudimova E., Экспериментальная психология 2024 Т. 17 № 3 С. 4–15
The work is aimed at studying the performance of emotion recognition of people of different sex and age, expressed without specified criteria, in association with «emotional families». The materials of an empirical online research obtained with the help of the crowdsourcing service «Yandex. Toloka», in which 3,590 tes- ters took part. The subjects guessed one ...
Added: December 26, 2024
Kolmogorova A., Хлебникова В. А., Человек: образ и сущность. Гуманитарные аспекты 2025 № 2(62) С. 45–68
В статье рассматриваются результаты применения методологии, известной как Байесовская сыворотка правды (BTS), в задаче эмоциональной разметки текстов для последующего обучения нейросетевых моделей. Суть метода состоит в том, что информантов сначала просят оценить некоторый феномен со своей собственной точки зрения, а затем – предсказать, какой ответ (или оценку) выберет наибольший процент других отвечающих на тот же ...
Added: November 29, 2024
Kulikova E., В кн.: Лингвистическая семантика в пространственном измерении: Словарь. Дискурс. Корпус.: Екатеринбург: Кабинетный ученый, 2024. Гл. 9.1 С. 399–411.
Added: November 29, 2024
Sysoeva T., Айрапетян Е. А., Психологические исследования: электронный научный журнал 2023 Т. 16 № 92 Статья 1
The current study is aimed to investigate the differences in emotion recognition among representatives of Armenian and Russian cultures. A preliminary study demonstrated that Armenians, unlike Russians, tend to control the expression of anger towards in-group members in greater extent. One hypothesis explaining the cultural influence on emotion recognition suggests that expression norms, which require ...
Added: January 29, 2024
Kartasheva A., Технологос 2020 № 2 С. 15–24
The article examines approaches to emotion recognition in intelligent systems from the point of view of methodological grounds. In interdisciplinary research, where it is necessary to combine approaches from different fields, we face terminological uncertainty, since the problem of describing the emotional sphere is solved by different researchers in line with several main approaches.
First, the ...
Added: October 20, 2023
A. V. Savchenko, L. V. Savchenko, Information Sciences 2023 Vol. 648 Article 119540
This article introduces the novel technique to reduce the computation time for classifying a sequence of observations (frames), such as a video stream, where each observation is described by high-dimensional embeddings extracted by a deep neural network. By using the methodology of granular computing, an observed sequence is represented at various scales using different frame ...
Added: August 27, 2023
Savchenko A., / Series Computer Science "arxiv.org". 2022.
In this paper, we present the results of the HSE-NN team in the 4th competition on Affective Behavior Analysis in-the-wild (ABAW). The novel multi-task EfficientNet model is trained for simultaneous recognition of facial expressions and prediction of valence and arousal on static photos. The resulting MT-EmotiEffNet extracts visual features that are fed into simple feed-forward ...
Added: October 21, 2022
Savchenko A., , in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).: IEEE, 2022. P. 2358–2365.
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on Affect-Net. The predictions for sequential frames are smoothed ...
Added: August 29, 2022
Savchenko A., Savchenko L., Makarov I., IEEE Transactions on Affective Computing 2022 Vol. 13 No. 4 P. 2132–2143
In this paper, behaviour of students in the e-learning environment is analyzed. The novel pipeline is proposed based on video facial processing. At first, face detection, tracking and clustering techniques are applied to extract the sequences of faces of each student. Next, a single efficient neural network is used to extract emotional features in each ...
Added: July 14, 2022