Smart Speech Emotion Recognition System Using MFCC and LSTM

Abstract: Speech Emotion Recognition (SER) aims to automatically detect human emotions from spoken language using computational methods. In this study, we propose a deep learning approach that leverages Mel Frequency Cepstral Coefficients (MFCC) features extracted from speech signals. A Long Short-Term Memory (LSTM) neural network is trained to classify emotions into seven categories. The model achieves a validation accuracy of approximately 93.93%. Extensive experiments on spectrogram and waveform visualizations reveal significant distinctions among different emotions, highlighting the potential of MFCC-based SER systems