  Herein, we have compared the performance of SVM and MLP in emotion
recognition using speech and song channels of the RAVDESS dataset. We have
undertaken a journey to extract various audio features, identify optimal
scaling strategy and hyperparameter for our models. To increase sample size, we
have performed audio data augmentation and addressed data imbalance using
SMOTE. Our data indicate that optimised SVM outperforms MLP with an accuracy of
82 compared to 75%. Following data augmentation, the performance of both
algorithms was identical at ~79%, however, overfitting was evident for the SVM.
Our final exploration indicated that the performance of both SVM and MLP were
similar in which both resulted in lower accuracy for the speech channel
compared to the song channel. Our findings suggest that both SVM and MLP are
powerful classifiers for emotion recognition in a vocal-dependent manner.
