Comparative Study of Classification Models for Emotion Detection from Speech
Shibraj Basak
, Prolay Ghosh
Emotion Detection, SVM, CNN, MLP, RAVDESS, Machine Learning.
The detection of emotions from speech is the aim of this paper. Speech consists of anger, joy and fear have very high and wide range in pitch, whereas Speech consists of sad and tired emotion have very low pitch. Speech Emotion detection technology can recognize human emotions to help machines better for understanding intentions of a user to improve the human-computer interaction. Classification models named Convolutional Neural Network (CNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP) based on mainly Mel Frequency Cepstral Coefficient (MFCC) feature to detect emotion have been presented here. The models have been trained to distinguish eight different emotions such as calm, neutral, angry, sad, happy, disgust, fear, surprise. The proposed work shows that CNN works best on RAVDESS dataset rather than MLP, SVM and records an accuracy of 63.88%.
"Comparative Study of Classification Models for Emotion Detection from Speech", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.837 - 840, March-2023, Available :https://ijsdr.org/papers/IJSDR2303133.pdf
Volume 8
Issue 3,
March-2023
Pages : 837 - 840
Paper Reg. ID: IJSDR_204593
Published Paper Id: IJSDR2303133
Downloads: 000347210
Research Area: Computer Science & Technology
Country: Nadia, West Bengal, India
DOI: http://doi.one/10.1729/Journal.33564
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
