  In this paper, we use several techniques with conventional vocal feature
extraction (MFCC, STFT), along with deep-learning approaches such as CNN, and
also context-level analysis, by providing the textual data, and combining
different approaches for improved emotion-level classification. We explore
models that have not been tested to gauge the difference in performance and
accuracy. We apply hyperparameter sweeps and data augmentation to improve
performance. Finally, we see if a real-time approach is feasible, and can be
readily integrated into existing systems.
