Speech Emotion Recognition Using Machine Learning Approach

DOI

10.2991/978-94-6463-136-4_50How to use a DOI?

Keywords

Emotions; Audio Signal; Random Forest (RF); Multilayer Perceptron (MLP); Support Vector Machine (SVM) Convolution Network (CNN); Decision Tree (DT); Ravdess Dataset; Classification

Abstract

Nowadays, emotion recognition and classification plays a vital role in the field of Human-Computer Interaction (HCI). Emotions are being recognized through behaviors of body such as facial expression, voice tone, and body movement. The present research considers Speech Emotion Recognition (SER) as one of the foremost used modality to identify emotions. SER dataset contains the four different datasets, Ravdess dataset is used in this project. This mechanism is used due to its high temporal resolution with no risks and less cost. Over the last decades, many researchers involved SER signals in sequence to cope up with Brain-Computer Interface (BCI) to detect emotions. It includes removing noises from audio signals, extracting temporal or spectral features from the audio signals, analysis on time or frequency domain respectively, and eventually, designing a multi-class classification strategy. The paper discusses the approach of identifying and classifying human emotions based on audio signals. The approach used machine learning technique such as Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Convolution Network (CNN), and Decision Tree (DT) Models for classification. The obtained experimental result seems to be promising with good accuracy in the emotion classification.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. G. Shaila
AU  - A. Sindhu
AU  - L. Monish
AU  - D. Shivamma
AU  - B. Vaishali
PY  - 2023
DA  - 2023/05/01
TI  - Speech Emotion Recognition Using Machine Learning Approach
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 592
EP  - 599
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_50
DO  - 10.2991/978-94-6463-136-4_50
ID  - Shaila2023
ER  -