July 2020
Intermediate to advanced
464 pages
17h 2m
English
Content preview from Mathematical Methods in Interdisciplinary Sciences
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3A Survey of Classification Techniques in Speech Emotion Recognition
Tanmoy Roy1, Tshilidzi Marwala1, and Snehashish Chakraverty2
1Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa
2Department of Mathematics, National Institute of Technology, Rourkela, Odisha, 769008, India
3.1 Introduction
Speech recognition models are presently in a very advanced state, and examples such as Siri, Cortana, and Alexa are the technical marvels that not can only hear us comfortably, but those bots can reply to us with equal comfort as well. However, speech recognition systems cannot detect the underlying emotion in speech signals. Speech emotion recognition (SER) is the field of study that explores the avenues of detecting emotions concealed in speech signals. The initial study on emotion started as a study of psychology and acoustics of emotions. The first detailed study on emotions was reported way back in 1872 by Charles Darwin [1]. Fairbanks and Pronovost [2] were among the first who studied pitch of voice during simulated emotion. Since the late 1950s, there has been a significant increase in interest by researchers regarding the psychological and acoustic aspects of emotion [3–6]. However, in the year 1995, Picard [7] introduced the term “affective computing,” and after that, the study of emotional states has become an integral part of artificial intelligence (AI) research.
SER is a machine learning (ML) problem where the speech ...
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