# Enhancing Multimodal Emotion Recognition through Attention Mechanisms in BERT and CNN Architectures
## 1 Introduction
This capability is advantageous for emotion analysis, as CNNs effectively capture emotions that appear rapidly and with marked intensity. Such focused analysis enables the model to discern between subtle and robust emotional cues, which is crucial in situations where emotions may intensify suddenly or peak briefly, thus ensuring a thorough understanding of the emotional landscape. Text also serves as a crucial medium for expressing and deciphering emotions. Unlike spoken words and physical expressions that often provide direct emotional cues, written language offers nuanced indicators through word choice, phrasing, and punctuation, enabling authors to convey a wide spectrum of emotions, from joy and excitement to sadness and despair. However, a major challenge in developing text-based models is the need for extensive, relevant data. The effectiveness and accuracy of these models depend heavily on the availability and accessibility of large, task-specific datasets. The greater the dataset's size and diversity, the more effectively the model can generalize and adapt to real-world scenarios. To address this challenge, the application of transfer learning and/or the utilization of pre-trained models have emerged as promising solutions. These techniques leverage the knowledge gained from one task and apply it to a related task, potentially reducing the need for extensive datasets specific to each challenge. Our study introduced a multimodal emotion-recognition system that combines speech and text data. First, a speech module was crafted using CNNs to extract patterns from the mel spectrograms, to produce a fully connected (FC) layer. This method excels in revealing intricate details in the time and frequency domains. Next, we employed a pre-trained BERT-based model for our text component. BERT's 12-layer bidirectional structure offers a profound understanding of textual semantics. Leveraging BERT's extensive training on large-scale datasets allows for a more enriched emotional representation. Post-BERT processing involved an FC layer for reducing dimensionality, a Bi-GRU for contextual understanding, and another FC layer to link features to specific emotions. These refined outputs were fed into our fusion module, thereby strengthening our emotion-detection mechanism. Finally, the fusion process involves an attention-based mechanism to balance the speech and text contributions. It allocates attention scores, prioritizing the most pertinent modality based on the context. We standardized the feature dimensions using an FC layer for unbiased attention allocation. The consolidated features were then passed through a softmax layer to classify the embedded emotion, ensuring comprehensive emotion analysis. This study introduced several vital contributions that deserve special emphasis. The research:  
* Unveiled an innovative multimodal emotion-detection method that surpassed the precision of prevailing benchmark models. This groundbreaking technique will facilitate future studies on emotion detection;
* Innovatively leveraged convolutional neural networks and a fully connected layer to extract mel spectrogram features, offering enhanced accuracy and bridging the divide between raw speech and emotional nuances;
* Employed a pre-trained BERT-based model to encode textual data effectively, capitalizing on its bidirectional transformer encoders to capture context-rich semantic information;
* Employed an attention-based fusion mechanism for a bimodal system that emphasizes speech and text, dynamically prioritizes modalities, standardizes feature vectors, and utilizes multistage processing to enhance sentiment classification accuracy. * Conducted comprehensive tests on two benchmark datasets, CMU-MOSEI and MELD, where the outcomes clearly highlighted the superior performance of our approach in emotion recognition. This article is arranged as follows. Section 2 provides a summary of pertinent research in the realm of multimodal emotion recognition. Section 3 describes the overall scheme of the proposed model.