PAPERS – Page 4

Using Eye Tracking to Investigate Interaction Between Humans and Virtual Agents

Leave a Comment / PAPERS, UV / By Terry Amorese

Amorese, T., Greco, C., Cuciniello, M., Buono, C., Palmero, C., Buch-Cardona, P., Escalera, S., Torres, M. I., Cordasco, G., & Esposito, A. (2022). Using Eye Tracking to Investigate Interaction Between Humans and Virtual Agents. 2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), 125–132. https://doi.org/10.1109/CogSIMA54611.2022.9830686 Repository link: https://iris.unicampania.it/handle/11591/476848

Multimodal Feature Evaluation and Fusion for Emotional Well-Being Monitorization

Leave a Comment / PAPERS, UPV / By raquel

Zubiaga, I., & Justo, R. (2022). Multimodal Feature Evaluation and Fusion for Emotional Well-Being Monitorization. In A. J. Pinho, P. Georgieva, L. F. Teixeira, & J. A. Sánchez (Eds.), Pattern Recognition and Image Analysis (pp. 242–254). Springer International Publishing. https://doi.org/10.1007/978-3-031-04881-4_20 Repository link: https://addi.ehu.es/handle/10810/59447

Multimodal Feature Evaluation and Fusion for Emotional Well-Being Monitorization

Leave a Comment / PAPERS, UPV / By raquel

Humanoid and android robots in the imaginary of adolescents, young adults and seniors

Leave a Comment / PAPERS, UV / By annaesp

Esposito, A., Cuciniello, M., Amorese, T., Vinciarelli, A., & Cordasco, G. (2022). Humanoid and android robots in the imaginary of adolescents, young adults and seniors. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-022-03806-z Repository link: https://iris.unicampania.it/handle/11591/468615

A Proposal for Multimodal Emotion Recognition Using Aural Transformers and Action Units on RAVDESS Dataset

Leave a Comment / PAPERS, UGR / By Zoraida Callejas

Emotion recognition is attracting the attention of the research community due to its multiple applications in different fields, such as medicine or autonomous driving. In this paper, we proposed an automatic emotion recognizer system that consisted of a speech emotion recognizer (SER) and a facial emotion recognizer (FER). For the SER, we evaluated a pre-trained …

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Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension

Leave a Comment / PAPERS, UGR / By Zoraida Callejas

Fernández-Martínez, F., Luna-Jiménez, C., Kleinlein, R., Griol, D., Callejas, Z., & Montero, J. M. (2022). Fine-Tuning BERT Models for Intent Recognition Using a Frequency Cut-Off Strategy for Domain-Specific Vocabulary Extension. Applied Sciences, 12(3), 1610. https://doi.org/10.3390/app12031610

Age and gender effects on the human’s ability to decode posed and naturalistic emotional faces

Leave a Comment / PAPERS, UV / By annaesp

Esposito, A., Amorese, T., Cuciniello, M., Riviello, M. T., & Cordasco, G. (2022). Age and gender effects on the human’s ability to decode posed and naturalistic emotional faces. Pattern Analysis and Applications, 25(3), 589–617. https://doi.org/10.1007/s10044-021-01049-w

Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning

Leave a Comment / PAPERS, UGR / By Zoraida Callejas

Emotion Recognition is attracting the attention of the research community due to the multiple areas where it can be applied, such as in healthcare or in road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For the speech-based modality, we evaluated several transfer-learning techniques, …

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Synthetic vs Human Emotional Faces: What Changes in Humans’ Decoding Accuracy

Leave a Comment / PAPERS, UV / By annaesp

Amorese, T., Cuciniello, M., Vinciarelli, A., Cordasco, G., & Esposito, A. (2022). Synthetic vs Human Emotional Faces: What Changes in Humans’ Decoding Accuracy. IEEE Transactions on Human-Machine Systems, 52(3), 390–399. https://doi.org/10.1109/THMS.2021.3129714

An empirical assessment of deep learning approaches to task-oriented dialog management

Leave a Comment / PAPERS, UGR / By Zoraida Callejas

Matějů, L., Griol, D., Callejas, Z., Molina, J. M., & Sanchis, A. (2021). An empirical assessment of deep learning approaches to task-oriented dialog management. Neurocomputing, 439, 327–339. https://doi.org/10.1016/j.neucom.2020.01.126 Repository link: https://digibug.ugr.es/bitstream/handle/10481/88561/2021_Mateju_Preprint.pdf