# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
## 2 Literature Review  
The study [11] suggested the Deep Learning (DL) technique is used to propose an emotion identification system based on emotional Big Data. Big Data includes audio and video. The suggested system processes voice signals in the frequency domain to create Mel-spectrograms, which can be used as images. Next, the Mel-spectrogram is supplied to a Convolutional Neural Network (CNN). To process video signals, the CNN is fed representative frames from a segment. The study [12] suggested how well a Long Short-Term Memory (LSTM) mechanism based on DL can identify emotions in text. The research used the Emotion classification dataset, which categorizes emotions into six distinct sets. The findings of the experiment show that, in comparison to other learning approaches, LSTM based text emotion categorization offers considerably greater accuracy. The research [13] proposed a speech-text multimodal emotion detection model to improve emotional recognition system performance. The CNN and LSTM were combined in binary channels to learn acoustic emotion features, while an effective Bidirectional-LSTM network was used to capture textual features. Evolution of the Cumulative Attribute Approach in Computer Vision Regression initiated by [14] in 2013, the Cumulative Attribute (CA) approach in computer vision regression has significantly evolved, addressing complex challenges in fields like age and crowd density estimation. This novel approach is centered around mapping sparse and imbalanced low-level visual features to a 'cumulative attribute space', where each dimension is semantically meaningful and reflects the continuous nature of scalar outputs such as age or people count. By ensuring both discriminative clarity and cumulative conditioning among attributes, CA effectively utilizes the correlation among adjacent scalar values, showcasing substantial improvements in accuracy and efficiency, especially in scenarios with limited and unevenly distributed training data. The study [15] introduced a new kind of Deep Neural Networks (DNN) that can use text, video, and audio to recognize emotions. Aiming to learn the representation for each modality and the best-combined representation to get the best prediction, the proposed DNN architecture's independent and shared layers work together. Building upon this foundation, further extended the CA approach in 2019 to multifaceted tasks like head pose estimation and color constancy [16]. The essence of CA in this expansion lies in its ability to innovatively map input features to cumulative attributes, capturing intricate correlations between various target values. This method overcomes the limitations of traditional regression models by addressing the interdependencies they often ignore, leading to a more holistic and accurate analysis of multivariate data. Originally effective in single-output problems, the extension of CA into multi-output regression underlines its versatility and potential in broader applications. The implementation of CA involves a two-stage process: the initial stage focuses on attribute learning--mapping from the feature space to the CA space, followed by the second stage, which maps these attributes to the output space. This approach significantly enhances the capability of regression models in visual tasks by leveraging the target space and employing attribute functions corresponding to various target variables. The article [17] focuses on evaluating various pre-trained language models' performance and environmental impact in text analysis. It highlights the significant trade-offs between model efficiency and ecological footprint in natural language processing, underlining the importance of environmental considerations in developing and applying these technologies in sentiment analysis. The study [18] suggested the methods also neglect merging features from many sources for use in machine learning. Research should focus on developing LSTM-based models capable of handling multi-modal feature fusion and fully accounting for utterance relations.