Speech emotion recognition and classification using hybrid deep CNN and BiLSTM model

Accurate emotion detection from speech utterances has been a challenging and active research affair recently. Speech emotion recognition (SER) systems play an essential role in Human-machine interaction, virtual reality, emergency services, and many other real-time systems. It is an open-ended problem as subjects from different regions and lingual backgrounds convey emotions altogether differently. The conventional approach used low-level periodic features from audio samples like energy, pitch, etc., for classification but was not efficient enough to detect emotions accurately and not generalized. With the recent advancements in computer vision and neural networks extracting high-level features and more accurate recognition can be achieved. This study proposes an ensemble deep CNN + Bi-LSTM-based framework for speech emotion recognition and classification of seven different emotions. The paralinguistic log Mel-frequency spectral coefficients (MFSC) is used as a feature to train the proposed architecture. The proposed Hybrid model is validated with TESS and SAVEE datasets. Experimental results have indicated a classification accuracy of 96.36%. The proposed model is compared with existing models, proving the superiority of the proposed hybrid deep CNN and Bi-LSTM model.