  Accomplishments in the field of artificial intelligence are utilized in the
advancement of computing and making of intelligent machines for facilitating
mankind and improving user experience. Emotions are rudimentary for people,
affecting thinking and ordinary exercises like correspondence, learning and
direction. Speech emotion recognition is domain of interest in this regard and
in this work, we propose a novel mel spectrogram learning approach in which our
model uses the datapoints to learn emotions from the given wav form voice notes
in the popular CREMA-D dataset. Our model uses log mel-spectrogram as feature
with number of mels = 64. It took less training time compared to other
approaches used to address the problem of emotion speech recognition.
