Relevant bibliographies by topics / Open multimodal emotion corpus

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Academic literature on the topic 'Open multimodal emotion corpus'

Author: Grafiati
Published: 3 June 2025
Last updated: 22 June 2025

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Journal articles on the topic "Open multimodal emotion corpus"

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Dychka, Ivan, Ihor Tereikovskyi, Andrii Samofalov, Lyudmila Tereykovska, and Vitaliy Romankevich. "MULTIPLE EFFECTIVENESS CRITERIA OF FORMING DATABASES OF EMOTIONAL VOICE SIGNALS." Cybersecurity: Education, Science, Technique 1, no. 21 (2023): 65–74. http://dx.doi.org/10.28925/2663-4023.2023.21.6574.

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Ekman, P. (2005). Basic Emotions. In Handbook of Cognition and Emotion (p. 45–60). John Wiley & Sons, Ltd. https://doi.org/10.1002/0470013494.ch3 Bachorowski, J.-A., & Owren, M. J. (1995). Vocal Expression of Emotion: Acoustic Properties of Speech Are Associated With Emotional Intensity and Context. Psychological Science, 6(4), 219–224. https://doi.org/10.1111/j.1467-9280.1995.tb00596.x Hirschberg, J. (2006). Pragmatics and Intonation. In The Handbook of Pragmatics (eds L.R. Horn and G. Ward). https://doi.org/10.1002/9780470756959.ch23 Tereykovska, L. (2023). Methodology of automated recognition of the emotional state of listeners of the distance learning system [Dissertation, Kyiv National University of Construction and Architecture]. Institutional repository of National transport university. http://www.ntu.edu.ua/nauka/oprilyudnennya-disertacij/ Kominek, J., & Black, A. (2004). The CMU Arctic speech databases. SSW5-2004. https://www.lti.cs.cmu.edu/sites/default/files/CMU-LTI-03-177-T.pdf (date of access: 01.06.2023) Zhou, K., Sisman, B., Liu, R., & Li, H. (2022). Emotional voice conversion: Theory, databases and ESD. Speech Communication, 137, 1–18. https://doi.org/10.1016/j.specom.2021.11.006 Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005). A database of German emotional speech. In Interspeech 2005. ISCA. https://doi.org/10.21437/interspeech.2005-446 Livingstone, S. R., & Russo, F. A. (2018). The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE, 13(5), Стаття e0196391. https://doi.org/10.1371/journal.pone.0196391 James, J., Tian, L., & Inez Watson, C. (2018). An Open Source Emotional Speech Corpus for Human Robot Interaction Applications. In Interspeech 2018. ISCA. https://doi.org/10.21437/interspeech.2018-1349 10) Costantini, G., Iaderola, I., Paoloni, A., & Todisco, M. (2014). EMOVO Corpus: an Italian Emotional Speech Database. У Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), 3501–3504, Reykjavik, Iceland. European Language Resources Association (ELRA).
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Ivanina, Ekaterina O., Anna D. Tokmovtseva, and Elizaveta V. Akelieva. "EmoEye: Eye-Tracking and Biometrics Database for Emotion Recognition." Lurian Journal 4, no. 1 (2023): 8–20. http://dx.doi.org/10.15826/lurian.2023.4.1.1.

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Emotion recognition using Machine Learning algorithms is often used both in science and commerce. Responding to the demand for deep learning techniques of automatic emotion detection using biological signals and our own business needs as a neuromarketing laboratory, we created a large dataset of eye tracking and biometrics data suitable for emotion recognition tasks. The EmoEye database sample consisted of 200 people (147 women, 49 men, 4 non-binary individuals; 27.46 ± 11.45 years old). Each respondent was asked to view 316 images from the Open Affective Standardized Image Set (OASIS) and rate them on arousal and valence scales from the Self-Assessment Manikin questionnaire. Eye tracking, galvanic skin response (GSR), and photoplethysmogram were recorded throughout the experiment. Demographic data was also collected for each respondent. The image ratings on the valence scale did not differ statistically from the standard ratings of the corresponding images for the original stimulus base. The overall distribution trends of ratings on both scales for different categories of images were similar for standard ratings and ratings obtained from our respondents. As a result of this study, a corpus of GSR, heart rate variability and eye movement reactions data (fixation coordinates; fixation duration; average pupil size for the right and left eye) was compiled and successfully trained on a multimodal neural network algorithm within our laboratory and is ready for further implementation.
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Komninos, Nickolas. "Discourse Analysis of the 2022 Australian Tennis Open: A Multimodal Appraisal Perspective." HERMES - Journal of Language and Communication in Business, no. 63 (October 27, 2023): 83–98. http://dx.doi.org/10.7146/hjlcb.vi63.140134.

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This article presents a preliminary analysis of a corpus of texts relating to the 2022 Australian Tennis Open using a multimodal appraisal framework. The study utilises quantitative and qualitative content analysis to examine media reports, official statements, and public reactions to the incident, which centred around Novak Djokovic's vaccination status. The analysis focusses on assessing how evaluative language contributes to community-building and identifies the underlying values, beliefs, and evaluations that shape stakeholders' emotional, cognitive, and behavioural responses.The appraisal framework, encompassing attitude, engagement, and graduation, serves as a comprehensive tool for categorising resources that express evaluation. Furthermore, the article delves into the application of appraisal analysis within the context of multimodal and online discourse, encompassing various platforms such as newspapers, television, radio, YouTube, Twitter, Instagram, blogs, official political statements, and court rulings. By examining these diverse media, the study seeks to investigate the dynamic discourse interplay surrounding the 2022 Australian Open, highlighting the pivotal role of evaluative communication in fostering alignment among readers through shared values and attitudes.The preliminary findings suggest that access to greater semiotic recourses increases consensus. The gains from using this interpretative framework are an asset, facilitating the coding of a large data set and attending the different manifestations of discourses around the player’s participation. As discourse continues to shape societal narratives, this multimodal appraisal investigation contributes to our understanding of the complex dynamics inherent in discourse construction and the influence of evaluative language in shaping collective perception.
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OBAMA, FRANCK DONALD, and Olesya Viktorovna Lazareva. "The problem of translating the verbal component of political cartoons in English, Russian and French." Litera, no. 4 (April 2025): 234–48. https://doi.org/10.25136/2409-8698.2025.4.72950.

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This article explores the linguistic and cultural characteristics of the verbal component in polycode political cartoons in Russian, English, and French, focusing on how these factors affect interpretation and perception in diverse sociocultural contexts. As a form of multimodal discourse, political cartoons integrate visual and verbal elements to produce complex ironic or sarcastic messages, whose translation presents both methodological and practical challenges. The object of the study is polycode political texts, while the subject is the verbal component, often saturated with cultural allusions, precedent phenomena, and stylistically marked language. The analysis demonstrates that even neutral or positively charged expressions, when juxtaposed with dark, absurd, or hyperbolic imagery, convey layered satirical meaning, which the translator must decode and render effectively. In intercultural communication, such texts often require not just linguistic equivalence but contextual adaptation and explanatory additions to preserve both the semantic and emotional depth of the original. The methodological framework of the study includes qualitative analysis, linguocultural and discourse-based approaches, as well as comparative and classification methods. The research material comprises a corpus of 90 political cartoons (30 in each language), selected through continuous sampling from open online sources. Based on the analysis, the article proposes an original typology of translatability for the verbal component, which includes three categories: fully translatable elements, partially translatable expressions, and those requiring adaptation or explanatory commentary. The choice of translation strategy depends on the genre specifics, visual context, and broader sociocultural conditions. Special attention is paid to the translator's role as an interpreter and cultural mediator, whose work demands a high degree of creativity, cultural awareness, and contextual sensitivity. The scientific novelty of the study lies in the development of a structured typology of translatability for the verbal component in political cartoons. This framework allows for a more precise selection of translation strategies based on textual characteristics, communicative goals, and cultural background. The findings contribute to a deeper understanding of cross-cultural interpretation mechanisms in satirical genres and expand the theoretical and practical foundation of multimodal translation studies.
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Majeed, Adil, and Hasan Mujtaba. "UMEDNet: a multimodal approach for emotion detection in the Urdu language." PeerJ Computer Science 11 (May 1, 2025): e2861. https://doi.org/10.7717/peerj-cs.2861.

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Emotion detection is a critical component of interaction between human and computer systems, more especially affective computing, and health screening. Integrating video, speech, and text information provides better coverage of the basic and derived affective states with improved estimation of verbal and non-verbal behavior. However, there is a lack of systematic preferences and models for the detection of emotions in low-resource languages such as Urdu. To this effect, we propose Urdu Multimodal Emotion Detection Network (UMEDNet), a new emotion detection model for Urdu that works with video, speech, and text inputs for a better understanding of emotion. To support our proposed UMEDNet, we created the Urdu Multimodal Emotion Detection (UMED) corpus, which is a seventeen-hour annotated corpus of five basic emotions. To the best of our knowledge, the current study provides the first corpus for detecting emotion in the context of multimodal emotion detection for the Urdu language and is extensible for extended research. UMEDNet leverages state-of-the-art techniques for feature extraction across modalities; for extracting facial features from video, both Multi-task Cascaded Convolutional Networks (MTCNN) and FaceNet were used with fine-tuned Wav2Vec2 for speech features and XLM-Roberta for text. These features are then projected into common latent spaces to enable the effective fusion of multimodal data and to enhance the accuracy of emotion prediction. The model demonstrates strong performance, achieving an overall accuracy of 85.27%, while precision, recall, and F1 scores, are all approximately equivalent. In the end, we analyzed the impact of UMEDNet and found that our model integrates data on different modalities and leads to better performance.
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Cu, Jocelynn, Katrina Ysabel Solomon, Merlin Teodosia Suarez, and Madelene Sta. Maria. "A multimodal emotion corpus for Filipino and its uses." Journal on Multimodal User Interfaces 7, no. 1-2 (2012): 135–42. http://dx.doi.org/10.1007/s12193-012-0114-8.

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Qi, Qingfu, Liyuan Lin, and Rui Zhang. "Feature Extraction Network with Attention Mechanism for Data Enhancement and Recombination Fusion for Multimodal Sentiment Analysis." Information 12, no. 9 (2021): 342. http://dx.doi.org/10.3390/info12090342.

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Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, including language, visual, and audio. Therefore, the traditional unimodal sentiment analysis method is no longer applicable, which requires the establishment of a fusion model of multimodal information to obtain sentiment understanding. In previous studies, scholars used the feature vector cascade method when fusing multimodal data at each time step in the middle layer. This method puts each modal information in the same position and does not distinguish between strong modal information and weak modal information among multiple modalities. At the same time, this method does not pay attention to the embedding characteristics of multimodal signals across the time dimension. In response to the above problems, this paper proposes a new method and model for processing multimodal signals, which takes into account the delay and hysteresis characteristics of multimodal signals across the time dimension. The purpose is to obtain a multimodal fusion feature emotion analysis representation. We evaluate our method on the multimodal sentiment analysis benchmark dataset CMU Multimodal Opinion Sentiment and Emotion Intensity Corpus (CMU-MOSEI). We compare our proposed method with the state-of-the-art model and show excellent results.
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MARTIN, JEAN-CLAUDE, RADOSLAW NIEWIADOMSKI, LAURENCE DEVILLERS, STEPHANIE BUISINE, and CATHERINE PELACHAUD. "MULTIMODAL COMPLEX EMOTIONS: GESTURE EXPRESSIVITY AND BLENDED FACIAL EXPRESSIONS." International Journal of Humanoid Robotics 03, no. 03 (2006): 269–91. http://dx.doi.org/10.1142/s0219843606000825.

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One of the challenges of designing virtual humans is the definition of appropriate models of the relation between realistic emotions and the coordination of behaviors in several modalities. In this paper, we present the annotation, representation and modeling of multimodal visual behaviors occurring during complex emotions. We illustrate our work using a corpus of TV interviews. This corpus has been annotated at several levels of information: communicative acts, emotion labels, and multimodal signs. We have defined a copy-synthesis approach to drive an Embodied Conversational Agent from these different levels of information. The second part of our paper focuses on a model of complex (superposition and masking of) emotions in facial expressions of the agent. We explain how the complementary aspects of our work on corpus and computational model is used to specify complex emotional behaviors.
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Buitelaar, Paul, Ian D. Wood, Sapna Negi, et al. "MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis." IEEE Transactions on Multimedia 20, no. 9 (2018): 2454–65. http://dx.doi.org/10.1109/tmm.2018.2798287.

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Jiang, Jiali. "Multimodal Emotion Recognition Based on Deep Learning." International Journal of Computer Science and Information Technology 5, no. 2 (2025): 71–80. https://doi.org/10.62051/ijcsit.v5n2.10.

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In recent years, multitask learning-based joint analysis of multiple emotions has emerged as a significant research topic in natural language processing and artificial intelligence. This approach aims to identify multiple emotion categories expressed in discourse by integrating multimodal information and leveraging shared knowledge across related tasks. Sentiment analysis, emotion recognition, and sarcasm detection constitute three closely interconnected tasks in affective computing. This paper focuses on these three tasks - sentiment analysis, emotion recognition, and sarcasm detection - while addressing current challenges in their research. The specific work includes the following three aspects: (1) Due to the limitations of datasets in the development of current Chinese multi-task learning models, this paper establishes a Chinese multi-task multi-modal dialogue emotion corpus to support the development of multi-task multi-modal sentiment analysis. The dataset is annotated with multiple task labels (such as sentiment, emotion, sarcasm, humor, etc.) and, for the first time, manually annotates the correlation between sentiment and emotion, as well as sarcasm and humor. Through scientific evaluation and analysis, it is demonstrated that the dataset possesses high quality and representativeness. (2) Based on the constructed dataset, this paper primarily considers three aspects: context interaction, multi-modal feature fusion, and multi-task learning, and proposes a multi-modal sentiment analysis model based on multi-task learning. Through experimental evaluation, the effectiveness of the model is demonstrated. (3) In response to the model proposed in (2), which fails to consider the interrelationships between tasks, this paper presents a multi-task learning model based on soft parameter sharing to learn the commonalities and differences between different tasks. Experimental results comparing with other advanced baselines demonstrate the innovation and efficiency of the proposed method.
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Dissertations / Theses on the topic "Open multimodal emotion corpus"

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Delahaye, Pauline. "Étude sémiotique des émotions complexes animales : des signes pour le dire." Thesis, Paris 4, 2017. http://www.theses.fr/2017PA040086.

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Cette thèse a pour objet la création d’un modèle théorique à destination de l’éthologie se présentant sous forme de grilles de lecture et de collection d’outils issus de la linguistique et de la sémiotique humaines. La finalité de ce modèle est de permettre l’étude zoosémiotique des émotions complexes au sein du règne animal. Il s’agit d’un travail pluridisciplinaire, interdisciplinaire et interthéoriciste, employant un corpus multimodal composé à la fois de textes théoriques linguistiques, d’études éthologiques et de supports multimédias, notamment des supports vidéo. Ce travail a été pensé dans un contexte d’absence de modèle théorique interdisciplinaire permettant l’étude de l’émotion animale, dans le but de permettre aux domaines des sciences du vivant et des sciences du langage de collaborer. Pour ce faire, il élabore tout d’abord un cadre théorique complet permettant une bonne entente des deux disciplines et revient sur tous les aspects essentiels (histoire, lexique, courant, idéologie, controverse). Par la suite, il présente le modèle théorique en explicitant sa construction et en donnant des exemples d’application. Dans la dernière partie la théorie est mise à l’épreuve par confrontation avec les données déjà existantes et approuvées par les éthologues. Cette partie permet de lister les forces et faiblesses du modèle, ainsi que les pistes de recherche, d’application et de réflexion qu’il ouvre au sujet de la sensibilité et de l’émotion animales<br>This PhD thesis’ object is the creation of a theoretical model for ethology. It is made of a collection of linguistics and human semiotics tools, organized into reading grids. This model’s aim is to allow the zoosemiotic study of complex emotions in animal kingdom. It’s a pluridisciplinary, interdisciplinary and intertheorist work with a multimodal corpus – including theoretical linguistics texts, ethology studies and multimedia contents, like videos. This work was created in a context of lack of interdisciplinary theoretical model. It was conceived with the aim of allow collaboration between life sciences and language sciences. To do so, we start first by building a complete theoretical frame for a good understanding between both disciplines. It goes over every main aspects – history, lexicology, schools, ideology, argument. Then, the theoretical model is introduced by explicating its construction and giving application examples. In the last part of the thesis, the theoretical model is tested by confrontation with existing and approved by ethologists datas. This part allows us to present strengths and weakness of the model – as well as lines of thought, research and application it opens
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Book chapters on the topic "Open multimodal emotion corpus"

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Huang, Zhaopei, Jinming Zhao, and Qin Jin. "Two-Stage Adaptation for Cross-Corpus Multimodal Emotion Recognition." In Natural Language Processing and Chinese Computing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44696-2_34.

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Deng, Jun, Nicholas Cummins, Jing Han, et al. "The University of Passau Open Emotion Recognition System for the Multimodal Emotion Challenge." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3005-5_54.

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Conference papers on the topic "Open multimodal emotion corpus"

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Ghosh, Shreya, Zhixi Cai, Parul Gupta, et al. "Emolysis: A Multimodal Open-Source Group Emotion Analysis and Visualization Toolkit." In 2024 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). IEEE, 2024. https://doi.org/10.1109/aciiw63320.2024.00023.

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Maham, Shiza, Abdullah Tariq, Bushra Tayyaba, Bisma Saleem, and Muhammad Hamza Farooq. "MMER: Mid-Level Fusion Strategy for Multimodal Emotion Recognition using Speech and Video Data." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2024. https://doi.org/10.1109/icosst64562.2024.10871145.

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Zhang, Zheng, Jeffrey M. Girard, Yue Wu, et al. "Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis." In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016. http://dx.doi.org/10.1109/cvpr.2016.374.

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Chou, Huang-Cheng, Wei-Cheng Lin, Lien-Chiang Chang, Chyi-Chang Li, Hsi-Pin Ma, and Chi-Chun Lee. "NNIME: The NTHU-NTUA Chinese interactive multimodal emotion corpus." In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2017. http://dx.doi.org/10.1109/acii.2017.8273615.

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Voloshina, Tatiana, and Olesia Makhnytkina. "Multimodal Emotion Recognition and Sentiment Analysis Using Masked Attention and Multimodal Interaction." In 2023 33rd Conference of Open Innovations Association (FRUCT). IEEE, 2023. http://dx.doi.org/10.23919/fruct58615.2023.10143065.

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Zhang, Zixing, Zhongren Dong, Zhiqiang Gao, et al. "Open Vocabulary Emotion Prediction Based on Large Multimodal Models." In MM '24: The 32nd ACM International Conference on Multimedia. ACM, 2024. http://dx.doi.org/10.1145/3689092.3689402.

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Deschamps-Berger, Theo, Lori Lamel, and Laurence Devillers. "Exploring Attention Mechanisms for Multimodal Emotion Recognition in an Emergency Call Center Corpus." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10096112.

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Horii, Daisuke, Akinori Ito, and Takashi Nose. "Design and Construction of Japanese Multimodal Utterance Corpus with Improved Emotion Balance and Naturalness." In 2022 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2022. http://dx.doi.org/10.23919/apsipaasc55919.2022.9980272.

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Clavel, Celine, and Jean-Claude Martin. "Exploring relations between cognitive style and multimodal expression of emotion in a TV series corpus." In 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII 2009). IEEE, 2009. http://dx.doi.org/10.1109/acii.2009.5349540.

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Chang, Chun-Min, Bo-Hao Su, Shih-Chen Lin, Jeng-Lin Li, and Chi-Chun Lee. "A bootstrapped multi-view weighted Kernel fusion framework for cross-corpus integration of multimodal emotion recognition." In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2017. http://dx.doi.org/10.1109/acii.2017.8273627.

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