Real-Time Speech Emotion Recognition Using Deep Learning Techniques

This paper presented an expanded IEEE-style article on real-time Speech Emotion Recognition using deep learning techniques. The proposed system uses TESS, EmoDB, and RAVDESS datasets, extracts multiple features including MFCC, Mel spectrogram, ZCR, RMS, and Chroma, and applies augmentation methods such as noise addition, pitch shifting, time stretching, and spectrogram shifting. The system compares MLP, CNN, ResNet50, and a hybrid CNN+Bi-LSTM model. The experimental results show that CNN+Bi-LSTM achieved the best performance with 95.42% accuracy, 94.90% precision, 94.62% recall, 94.75% F1-score, and 620 ms inference latency. The hybrid model outperformed baseline architectures because it combined CNN-based spectral-spatial learning with Bi-LSTM-based temporal sequence learning. Robustness testing also confirmed that the model maintains good performance under noise conditions. The proposed SER framework is suitable for real-time applications in healthcare, education, customer service, driver monitoring, virtual assistants, and social robotics. The study confirms that temporal modeling is essential for accurate emotion recognition from speech and that data augmentation improves practical robustness. Overall, the CNN+Bi-LSTM framework provides a strong foundation for intelligent emotion-aware systems.

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