Emotion Detection using Multimodal Deep Learning Techniques

Abstract

Emotion detection is a growing field in artificial intelligence. It aims to help machines understand human feelings through signals like speech, facial expressions, and text. Traditional systems usually depend on one type of input, which limits their accuracy since emotions are complex and expressed in various ways. To address this issue, this work suggests a multimodal deep learning method that combines features from text, speech, and images. Text data is represented using transformer-based embeddings. Audio signals are analyzed with recurrent neural networks that use Mel-Frequency Cepstral Coefficients (MFCCs). Facial features are extracted with convolutional neural networks (CNNs). These different features are merged into a single representation and classified using a deep neural network. Experimental results show that this multimodal framework outperforms unimodal models, leading to better accuracy, precision, and recall. This study emphasizes the potential of multimodal emotion detection in fields like online learning, healthcare, customer service, and human-computer interaction.

Introduction

Emotion detection is vital for improving human–computer interaction, but traditional single-modal methods—using only text, speech, or images—often struggle with real-world challenges like sarcasm, noise, or poor lighting. To address these limitations, the study proposes a multimodal deep learning framework that integrates text, speech, and facial image inputs. Text is processed using transformer-based models (e.g., BERT), speech is analyzed via MFCCs and recurrent neural networks, and images are processed with convolutional neural networks. Features from all modalities are fused and classified through a deep neural network to produce accurate emotion predictions.

Experiments on benchmark datasets show that the multimodal system outperforms unimodal approaches, providing higher reliability and accuracy across emotions such as happy, sad, angry, neutral, and surprised. By combining multiple signals, the framework can better handle real-world conditions, making it suitable for applications in healthcare, online learning, customer support, and other human–computer interaction contexts.

Conclusion

This work presents a multimodal deep learning approach for emotion detection that integrates text, speech, and facial image features to achieve higher accuracy and robustness. Unlike unimodal methods, which rely on a single source of data, the proposed framework combines complementary signals to better capture the complexity of human emotions. The experimental results show that the system performs well across different emotion categories such as happy, sad, angry, neutral, and surprise. The study highlights the potential of multimodal systems in real-world applications, including online education, healthcare, and customer service, where accurate emotion understanding is essential. Future enhancements may focus on using larger datasets, applying transformer-based fusion models, and enabling real-time emotion recognition for interactive AI systems.

References

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Copyright

Copyright © 2025 Suganthi D, Fowmitha I, Maha Sri K, Deepika B. 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.