Multimodal Emotion Recognition Using Attention-Based Deep Neural Networks

Abstract: Emotion recognition has become a significant research area in affective computing and human–computer interaction, as understanding human emotions plays a vital role in developing intelligent and responsive systems. Traditional unimodal emotion recognition systems rely on a single source of information such as speech, facial expressions, or text, which often leads to limited performance due to the absence of complementary contextual cues. To overcome these limitations, multimodal emotion recognition integrates multiple modalities—typically audio, visual, and textual data—to capture a more comprehensive representation of human affective states. This paper presents an attention-based deep neural network framework for multimodal emotion recognition. The proposed approach leverages deep feature extraction techniques using Convolutional Neural Networks (CNNs) for visual data, Recurrent Neural Networks (RNNs)/Long Short-Term Memory (LSTM) networks for audio sequences, and contextual embedding models for textual information. An attention mechanism is incorporated to dynamically assign weights to the most informative features across modalities, enabling the model to focus on emotionally salient cues while reducing irrelevant noise. The fusion of multimodal features is performed through a hybrid attention-based integration layer, enhancing the robustness and generalization capability of the system. The proposed model aims to improve classification accuracy across standard emotion categories such as happiness, sadness, anger, fear, and neutrality. Experimental evaluation on benchmark multimodal emotion datasets demonstrates that the attention-based fusion strategy significantly outperforms traditional unimodal and early-fusion approaches. The results highlight the effectiveness of attention mechanisms in capturing cross-modal dependencies and improving emotion prediction performance. This study contributes to the advancement of intelligent emotion-aware systems that can be applied in virtual assistants, mental health monitoring, smart education platforms, and interactive AI systems.

Keywords: Multimodal Emotion Recognition, Attention Mechanism, Deep Neural Networks, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Multimodal Fusion, Affective Computing, Speech Emotion Recognition, Facial Expression Analysis, Transformer Networks, Human–Computer Interaction, Cross-Modal Learning.