Laughter classification using 3D convolutional neural networks

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

ACM International Conference Proceeding Series

First Page

47

Last Page

51

Publication Date

10-26-2019

Abstract

Social signals express the attitude of human being in social situations. Laughter has been determined as an important social signal that can predict emotional information of people. It conveys different emotions such as happiness, surprise, fear, anger, and anxiety. Therefore, identifying and extracting emotions in the laughter is useful for estimating the emotional state of the user. Deep neural networks are replacing traditional methods because they perform more accurately. This paper presents work that detects the emotions in laughter by using audio features and running 3D Convolutional Neural Networks. The best rate of accuracy produced by 3D CNNs is 97.97%, which is higher than the results of our previous paper, which applied MLP and SVM on Iranian laughter dataset. © 2019 ACM.

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Digitial Object Identifier (DOI)

10.1145/3369114.3369142

Disciplines

Computer Sciences | Software Engineering

Keywords

Emotion recognition; Laughter; Neural networks (Computer science)

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