Emotion Detection using Voice Analysis Utilising EfficientNet and BiLSTM

Emotion recognition has become a crucial aspect of human-computer interaction, addressing the need for intelligent systems capable of understanding and responding to human emotions. This paper presents a voice-based emotion analysis model that integrates EfficientNet for feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for sequential processing. The model is trained on the Toronto Emotional Speech Set (TESS) dataset, leveraging acoustic features such as chroma features, spectral contrast, zero-crossing rate, and Mel-Frequency Cepstral Coefficients (MFCCs) to enhance feature representation. Standardization is applied before training to improve model performance. The proposed approach is trained for 50 epochs with a batch size of 32, achieving an accuracy of 82.50% and a macro-average F1 score of 82.52%. While the model demonstrates promising results, challenges remain, including dataset limitations, speaker variability, and the need for improved real-time processing. Future work will focus on addressing these challenges through model enhancements, dataset diversification, and the integration of multimodal emotion recognition techniques to further improve classification accuracy and generalization. The proposed voice-based emotion recognition model enhances real-time human-computer interaction by enabling systems to accurately interpret and respond to users' emotional states. This improvement has practical applications in areas such as virtual assistants, mental health monitoring, customer service, and personalized user experiences.