Comparative Analysis of Machine Learning Models for Emotion Classification in Speech Data

DOI

10.2991/978-94-6463-471-6_45How to use a DOI?

Keywords

Convolutional Neural Networks (CNN); Decision Trees; Emotion Recognition; LSTM Networks; Machine Learning Algorithms

Abstract

Understanding emotions is critical to many fields, including psychology, medicine, and human-computer interaction. The study uses datasets from RAVDESS, SAVEE, CREMA, and TESS which cover a wide spectrum of emotions, including neutral, surprise, happiness, sadness, disgust, anger, and fear to thoroughly investigate machine learning algorithms for emotion identification in audio data. Long short-term memory (LSTM) networks, decision trees, and convolutional neural networks (CNNs) are the three different models that are investigated. Decision trees provide simple classification, LSTMs extract temporal correlations from the data, and CNNs are excellent at extracting features from audio signals. Performance indicators like recall, F1, precision, and accuracy score are used in performance evaluation. Significantly, the CNN model outperforms Decision Trees and LSTM networks with 72% and 77%, respectively, in emotion categorization accuracy, reaching a remarkable 91%. This work offers insightful information about how well different machine learning models perform when it comes to audio-based emotion recognition. These realizations will have a big impact on developing trustworthy emotion detection systems for emotional computing, human-robot interaction, and mental health assessment. Future studies could investigate ensemble approaches or hybrid models to improve emotion detection capabilities and progress the creation of increasingly intricate and accurate emotion recognition systems.

Copyright

© 2024 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  - N. Siva
AU  - B. Venkata Sivaiah
AU  - G. Sai Kumar
AU  - G. Jaya Vardhan Raju
AU  - V. Sushvitha
AU  - G. Chaithanya
AU  - Sam Goundar
PY  - 2024
DA  - 2024/07/30
TI  - Comparative Analysis of Machine Learning Models for Emotion Classification in Speech Data
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 464
EP  - 474
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_45
DO  - 10.2991/978-94-6463-471-6_45
ID  - Siva2024
ER  -