  Identifying emotion from speech is a non-trivial task pertaining to the
ambiguous definition of emotion itself. In this work, we adopt a
feature-engineering based approach to tackle the task of speech emotion
recognition. Formalizing our problem as a multi-class classification problem,
we compare the performance of two categories of models. For both, we extract
eight hand-crafted features from the audio signal. In the first approach, the
extracted features are used to train six traditional machine learning
classifiers, whereas the second approach is based on deep learning wherein a
baseline feed-forward neural network and an LSTM-based classifier are trained
over the same features. In order to resolve ambiguity in communication, we also
include features from the text domain. We report accuracy, f-score, precision,
and recall for the different experiment settings we evaluated our models in.
Overall, we show that lighter machine learning based models trained over a few
hand-crafted features are able to achieve performance comparable to the current
deep learning based state-of-the-art method for emotion recognition.
