Emotions Classification from Speech with Deep Learning

Emotions are the essential parts that convey mean-ing to the interlocutors during social interactions. Hence, recog-nising emotions is paramount in building a good and natural affective system that can naturally interact with the human interlocutors. However, recognising emotions from social inter-actions require temporal information in order to classify the emotions correctly. This research aims to propose an architecture that extracts temporal information using the Temporal model of Convolutional Neural Network (CNN) and combined with the Long Short Term Memory (LSTM) architecture from the Speech modality. Several combinations and settings of the architectures were explored and presented in the paper. The results show that the best classifier achieved by the model trained with four layers of CNN combined with one layer of Bidirectional LSTM. Furthermore, the model was trained with an augmented training dataset with seven times more data than the original training dataset. The best model resulted in 94.25%, 57.07%, 0.2577 and 1.1678 for training accuracy, validation accuracy, training loss and validation loss, respectively. Moreover, Neutral (Calm) and Happy are the easiest classes to be recognised, while Angry is the hardest to be classified.

@article{Chowanda2022,
  title     = {Emotions Classification from Speech with Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {4},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Andry Chowanda and Yohan Muliono},
  doi       = {10.14569/IJACSA.2022.0130490},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130490}
}