An Emotion Detection System Based on Multi Least Squares Twin Support Vector Machine

Abstract

Posttraumatic stress disorder (PTSD),
bipolar manic disorder (BMD), obsessive compulsive
disorder (OCD), depression, and suicide are some
major problems existing in civilian and military life.
The change in emotion is responsible for such type of
diseases. So, it is essential to develop a robust and
reliable emotion detection system which is suitable
for real world applications. Detection of emotion in
speech can be applied in a variety of situations to
allocate limited human resources to clients with the
highest levels of distress or need, such as in
automated call centers or in a nursing home. In this
paper, we used a novel multi least squares twin
support vector machine classifier in order to detect
seven different emotions such as anger, happiness,
sadness, anxiety, disgust, panic, and neutral emotions.
The experimental result indicates better performance
of the proposed technique over other existing
approaches. The result suggests that the proposed
emotion detection system may be used for screening
of mental status.