## 5 Conclusions  
From the moment VR and AR avatars became deeply integrated into our society, the issue of how virtual objects in these environments react like real people has become increasingly significant. Contemporary AI no longer focuses solely on problem-solving but has also made notable advancements in areas such as music, art, and conversation. Therefore, for virtual objects in the metaverse to interact more naturally with real-world entities, precise emotional analysis is essential, whether it is to accurately represent user emotions using VR or AR avatars or to express AI emotions through avatars.  
This paper proposes an Emotion Analysis System (EAS) for accurate emotion analysis, which simultaneously recognizes the user's voice and facial expressions. EAS uses a microphone and camera to analyze the user's voice and facial expressions and then processes the results through a neural network to determine the user's emotions accurately. EAS consists of three modules:  
1. Facial Emotion Analysis Model (FEAM): This model receives facial expressions as input and classifies them into seven emotions using facial landmarks.
2. Voice Sentiment Analysis Model (VSAM): This model extracts voices from noisy environments using MycycleGAN to accurately identify emotions from the user's voice.
3. Comprehensive Sentiment Analysis Module (CSAM): This module receives the results from FEAM and VSAM and accurately analyzes the current emotions of the user.  
To validate the proposed system, two experiments were conducted. The first experiment tested the effectiveness of FEAM. It was observed that models with fewer layers quickly overfit. Simulation results showed that FEAM achieved an accuracy of 0.51 and an F1 score of A, while the CNN model achieved an accuracy of 0.5307 and an F1 score of A. FEAM completed the evaluation of test data in just 0.004 s per step, whereas the CNN model required 8.128 s per step. This indicates that reducing the input data and lightweighting the model can lead to faster processing.  
The second experiment tested the effectiveness of MCycleGAN. In noise handling, MCycleGAN showed lower error rates and lower discriminator loss functions compared to GAN and CycleGAN. This indicates that MCycleGAN maintains the original voice shape  
Figure 9: The loss of the generator and the loss of the discriminator during the training of each model over 40 epochs.  
while handling noise and that the processed data are not significantly different from the Clean Baby Sound domain, effectively deceiving the discriminator.  
However, the current EAS only analyzes user emotions and does not implement emotion-based expressions. Additionally, research into the application of analyzed emotions and related fields has not been conducted. Future research should focus on developing AI models that can generate appropriate facial expressions or voices based on emotions or explore applications of the analyzed emotions.  
Conceptualization, S.S. and Y.J.; Methodology, S.S.; Software, S.S.; Validation, Y.J.; Writing--original draft, S.S.; Writing--review & editing, Y.J.; Visualization, S.S.; Supervision, Y.J.; Project administration, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from 'AI-Hub' and are available '[[https://www.aihub.or.kr/](https://www.aihub.or.kr/)]([https://www.aihub.or.kr/](https://www.aihub.or.kr/))' with the permission of 'AI-Hub' (accessed on 12 January 2025).  
The authors declare no conflicts of interest.