  This paper proposes a Residual Convolutional Neural Network (ResNet) based on
speech features and trained under Focal Loss to recognize emotion in speech.
Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients
(MFCCs) have shown the ability to characterize emotion better than just plain
text. Further Focal Loss, first used in One-Stage Object Detectors, has shown
the ability to focus the training process more towards hard-examples and
down-weight the loss assigned to well-classified examples, thus preventing the
model from being overwhelmed by easily classifiable examples.
