Analysis of Human Emotion via Speech Recognition Using Viola Jones Compared with Histogram of Oriented Gradients (HOG) Algorithm with Improved Accuracy

Abstract

The objective of this study is to enhance the precision in predicting human emotions through speech signals.
This is achieved by introducing a novel approach, the Viola Jones (VJ) method, in contrast to the conventional
Histogram of Oriented Gradients (HOG) algorithm. In this research we used Toronto Emotional Speech Set
(TESS) as a dataset for this with a G-power of 0.8, alpha and beta values of 0.05 and 0.2, and a Confidence
Interval of 95%, sample size is calculated as twenty (ten from Group 1 and ten from Group 2). Viola Jones
(VJ) and Histogram of Oriented Gradients, both with the same amount of data samples (N=10), are used to
perform the prediction of human emotion recognition from speech signals. The performance of the proposed
viola jones is much greater than the accuracy rate of 88.65 percent achieved by the histogram of oriented
gradients classifier. This is because the success rate of the proposed viola jones is 95.66 percent. The level of
significance that was assessed to be attained by the research was p = 0.001 (p<0.05) which infers the two
groups are statistically significant. For the performance evaluation of human emotion classification from
speech data, the proposed Viola Jones (VJ) model achieves a greater level of precision than Histogram of
Oriented Gradients (HOG).