Overview of EmoSPeech at IberLEF 2024: Multimodal Speech-text Emotion Recognition in Spanish

• Autores: Ronghao Pan, José Antonio García Díaz, Miguel Ángel Rodríguez García , Francisco García Sánchez , Rafael Valencia García
• Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 73, 2024, págs. 359-368
• Idioma: inglés
• Títulos paralelos:
  • Resumen de EmoSPeech en IberLEF 2024: Reconocimiento de Emociones Multimodal Basado en Texto y Audio en Español
• Enlaces
  • Texto completo
• Resumen
  • español

    Este articulo resume la tarea EmoSPeech 2024, organizada en el taller IberLEF 2024, dentro del marco de la 40ª Conferencia Internacional de la Sociedad Española de Procesamiento del Lenguaje Natural (SEPLN 2024). El objetivo de esta tarea es investigar el campo del Reconocimiento Automático de Emociones, que está adquiriendo cada vez más importancia debido a su impacto en diversos campos, como la sanidad, la psicología, las ciencias sociales y el marketing. En concreto, se proponen dos subtareas que se evalúan por separado. La primera subtarea se refiere al análisis de emociones a partir de texto, que se centra en la extracción de características y la identificación de las más representativas características de cada emoción en un conjunto de datos creado a partir de situaciones de la vida real. La segunda subtarea se centra en el análisis de emociones desde una perspectiva multimodal, lo que requiere la construcción de una arquitectura más compleja para resolver este problema de clasificación. La clasificación incluye los resultados de 13 equipos diferentes, cada uno de los cuales propuso un enfoque novedoso del problema.

  • English

    This paper presents the EmoSPeech 2024 shared task, which was organized in the IberLEF 2024 workshop within the framework of the 40th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2024). The objective of this shared task is to study the field of Automatic Emotion Recognition (AER), which is becoming increasingly important due to its impact on various fields, such as healthcare, psychology, social sciences, and marketing. Specifically, two tasks are proposed and evaluated separately. The first task deals with AER from text, which focusing on feature extraction and identifying the most representative feature of each emotion in a dataset created from real-life situations. The second task deals with AER from a multimodal perspective, which requires the construction of a more complex architecture to solve this classification problem. The ranking includes the results of 13 different teams, each of which proposed a novel approach to the problem.

• Referencias bibliográficas
  • Almela, A., P. Cantos-Gómez, D. G.-M. no, and G. Alcaraz-Mármol. 2024. LACELL at EmoSPeech-IberLEF2024: Combining Linguistic Features and...
  • Boyd, R. L., A. Ashokkumar, S. Seraj, and J. W. Pennebaker. 2022. The development and psychometric properties of LIWC-22. Austin, TX: University...
  • Cañete, J., G. Chaperon, R. Fuentes, J. Ho, H. Kang, and J. Pérez. 2023. Spanish Pre-trained BERT Model and Evaluation Data. CoRR, abs/2308.02976.
  • Casals-Salvador, M., F. Costa, M. India, and J. Hernando. 2024. BSC-UPC at EmoSPeech-IberLEF2024: Attention Pooling for Emotion Recognition....
  • Cedeño-Moreno, D., M. Vargas-Lombardo, A. Delgado-Herrera, C. Caparrós-Láiz, and T. Bernal-Beltrán. 2024. UTP at EmoSPeech–IberLEF2024: Using...
  • Chaves-Villota, A., A. Jimenez, and A. Bahillo. 2024. UAH-UVA at EmoSPeech-IberLEF2024: A Transfer Learning Approach for Emotion Recognition...
  • Chenchah, F. and Z. Lachiri. 2016. Speech emotion recognition in noisy environment. In 2nd International Conference on Advanced Technologies...
  • Chiruzzo, L., S. M. Jiménez-Zafra, and F. Rangel. 2024. Overview of IberLEF 2024: Natural Language Processing Challenges for Spanish and other...
  • Conneau, A., A. Baevski, R. Collobert, A. Mohamed, and M. Auli. 2021. Unsupervised Cross-Lingual Representation Learning for Speech Recognition....
  • Conneau, A., K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov. 2020. Unsupervised...
  • de la Rosa, J., E. G. Ponferrada, M. Romero, P. Villegas, P. G. de Prado Salas, and M. Grandury. 2022. BERTIN: Efficient Pre-Training of a...
  • Ekman, P. 1992. Facial expressions of emotion: New findings, new questions. Psychological Science, 3(1):34–38.
  • Esteban-Romero, S., J. Bellver-Soler, I. Martín-Fernández, M. Gil-Martín, L. F. D’Haro, and F. Fernández-Martínez. 2024. THAU-UPM at EmoSPeech-IberLEF2024:...
  • Fahad, M. S., A. Ranjan, J. Yadav, and A. Deepak. 2021. A survey of speech emotion recognition in natural environment. Digit. Signal Process.,...
  • García-Baena, D., M. A. García-Cumbreras, and S. M. Jiménez-Zafra. 2024. SINAI at EmoSPeech-IberLEF2024: Evaluating Popular Tools and Transformers...
  • Gladun, A., J. Rogushina, and R. Martínez-Béjar. 2024. UKR at Emo-SPeech–IberLEF2024: Using Fine-tuning with BERT and MFCC Features for Emotion...
  • Hu, E. J., Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models....
  • Lagos-Ortiz, K., J. Medina-Moreira, and O. Apolinario-Arzube. 2024. UAE at EmoSPeech–IberLEF2024: Integrating Text and Audio Features with...
  • Liu, Y., M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov. 2019. RoBERTa: A Robustly Optimized...
  • Lugovic, S., I. Dunder, and M. Horvat. 2016. Techniques and applications of emotion recognition in speech. In P. Biljanovic, Z. Butkovic,...
  • Martinez-Romo, J., J. F. Huesca-Barril, L. Araujo, and E. de La Cal Marin. 2024. UNED-UNIOVI at EmoSPeech-IberLEF2024: Emotion Identification...
  • Mohammad, S. M. and F. Bravo-Marquez. 2017. WASSA-2017 Shared Task on Emotion Intensity. In A. Balahur, S. M. Mohammad, and E. van der Goot,...
  • Nguyen, N., X. Vu, C. Rigaud, L. Jiang, and J. Burie. 2021. ICDAR 2021 Competition on Multimodal Emotion Recognition on Comics Scenes. In...
  • Pan, R., J. A. García-Díaz, M. ´A. Rodríguez-García, and R. Valencia-García. 2024. Spanish MEACorpus 2023: A multimodal speech-text corpus...
  • Paredes-Valverde, M. A. and M. d. P. Salas-Zárate. 2024. Team ITST at EmoSPeech-IberLEF2024: Multimodal Speech-text Emotion Recognition in...
  • Pérez, J. M., D. A. Furman, L. A. Alemany, and F. M. Luque. 2022. RoBERTuito: a pre-trained language model for social media text in Spanish....
  • Plaza del Arco, F. M., S. M. Jiménez-Zafra, A. Montejo-Ráez, M. D. Molina-González, L. A. Ureña López, and M. T. Martín-Valdivia. 2021. Overview...
  • Sanh, V., L. Debut, J. Chaumond, and T. Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR, abs/1910.01108.
  • Soto, M., C. Macias, M. Cardoso-Moreno, T. Alcántara, O. García, and H. Calvo. 2024. CogniCIC at EmoSPeech-IberLEF2024: Exploring Multimodal...
  • Varghese, A. A., J. P. Cherian, and J. J. Kizhakkethottam. 2015. Overview on emotion recognition system. In 2015 international conference...
  • Villegas, M. 2023. MarIA: Spanish Language Models. In A. P. Rocha, L. Steels, and H. J. van den Herik, editors, Proceedings of the 15th International...
  • Zhang, D., Y. Yu, C. Li, J. Dong, D. Su, C. Chu, and D. Yu. 2024. MM-LLMs: Recent Advances in MultiModal Large Language Models. CoRR, abs/2401.13601.
  • Zheng, T. F., G. Zhang, and Z. Song. 2001. Comparison of Different Implementations of MFCC. J. Comput. Sci. Technol., 16(6):582–589.