  There are a variety of features of the human voice that can be classified as
pitch, timbre, loudness, and vocal tone. It is observed in numerous incidents
that human expresses their feelings using different vocal qualities when they
are speaking. The primary objective of this research is to recognize different
emotions of human beings such as anger, sadness, fear, neutrality, disgust,
pleasant surprise, and happiness by using several MATLAB functions namely,
spectral descriptors, periodicity, and harmonicity. To accomplish the work, we
analyze the CREMA-D (Crowd-sourced Emotional Multimodal Actors Data) & TESS
(Toronto Emotional Speech Set) datasets of human speech. The audio file
contains data that have various characteristics (e.g., noisy, speedy, slow)
thereby the efficiency of the ML (Machine Learning) models increases
significantly. The EMD (Empirical Mode Decomposition) is utilized for the
process of signal decomposition. Then, the features are extracted through the
use of several techniques such as the MFCC, GTCC, spectral centroid, roll-off
point, entropy, spread, flux, harmonic ratio, energy, skewness, flatness, and
audio delta. The data is trained using some renowned ML models namely, Support
Vector Machine, Neural Network, Ensemble, and KNN. The algorithms show an
accuracy of 67.7%, 63.3%, 61.6%, and 59.0% respectively for the test data and
77.7%, 76.1%, 99.1%, and 61.2% for the training data. We have conducted
experiments using Matlab and the result shows that our model is very prominent
and flexible than existing similar works.
