Version 1
: Received: 15 September 2024 / Approved: 16 September 2024 / Online: 16 September 2024 (09:59:26 CEST)
How to cite:
Yi, M.; Kwak, K.; Shin, J. KoMPT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, MFCC, and Pitch with Multimodal Transformer. Preprints2024, 2024091178. https://doi.org/10.20944/preprints202409.1178.v1
Yi, M.; Kwak, K.; Shin, J. KoMPT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, MFCC, and Pitch with Multimodal Transformer. Preprints 2024, 2024091178. https://doi.org/10.20944/preprints202409.1178.v1
Yi, M.; Kwak, K.; Shin, J. KoMPT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, MFCC, and Pitch with Multimodal Transformer. Preprints2024, 2024091178. https://doi.org/10.20944/preprints202409.1178.v1
APA Style
Yi, M., Kwak, K., & Shin, J. (2024). KoMPT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, MFCC, and Pitch with Multimodal Transformer. Preprints. https://doi.org/10.20944/preprints202409.1178.v1
Chicago/Turabian Style
Yi, M., KeunChang Kwak and JuHyun Shin. 2024 "KoMPT: A Multimodal Emotion Recognition Model Integrating KoELECTRA, MFCC, and Pitch with Multimodal Transformer" Preprints. https://doi.org/10.20944/preprints202409.1178.v1
Abstract
With the development of human-computer interaction, the importance of emotion recognition is increasing. Emotion recognition technology provides practical benefits in various industries such as improving user experience, education, and organizational productivity. In education, emotion recognition can enable real-time understanding of students' emotional states and provide tailored feedback, and in the workplace, by monitoring employees' emotional states, it can improve work performance and job satisfaction. Therefore, multimodal-based research that combines text, speech, and video data for emotion recognition is being conducted in various industries. In this study, we propose an emotion recognition method that combines text and speech data, reflecting the characteristics of the Korean language. For text, KoELECTRA is used to perform embedding, and for speech, MFCC and pitch analysis are used to extract features. Finally, a multimodal transformer model is proposed that combines these two data types to perform emotion recognition. The multimodal transformer model processes text and speech data separately, and then learns the interaction between the two modalities through a Cross-Modal Attention mechanism. Through this, the complementary information from text and speech is effectively combined, improving emotion recognition performance. Experimental results show that the proposed model outperforms single-modality models, achieving a high accuracy of 73.13% and an F1-Score of 0.7344 in emotion classification. This study contributes to the advancement of emotion recognition technology by combining various language and modality data, and higher performance can be expected through the integration of additional modalities in the future
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
