Comparative Analysis of Speech Emotion Recognition Models and Technique

A. Agrawal; A. Jain; B. Kaur; S. Jangid; K. Kadian; V. Dwivedi; S. Garhwal

doi:10.1109/CICTN57981.2023.10141044

Comparative Analysis of Speech Emotion Recognition Models and Technique

A. Agrawal, A. Jain, B. Kaur, S. Jangid, K. Kadian, V. Dwivedi, S. Garhwal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Speech Emotion Recognition (SER) refers to accurately predicting human emotions from their speech. The ability to predict emotions through speech signals is a motivating factor in achieving Human-Computer Interaction (HCI). This paper contains a comparative study of the existing research on speech emotion models. It makes use of the RAVDESS and SAVEE dataset containing audio input. The study of speech emotion recognition is made on SVM, CNN, KNN, MLP, Decision Tree, XGBoost, and Random Forest models. This paper presents a comparative analysis of the models highlighting the accuracy, F1 Score, bar plots, and loss graphs of the same. The paper also highlights the significant future areas for study in speech emotion recognition.

Original language	English
Title of host publication	2023 International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023
Publisher	IEEE
ISBN (Electronic)	9798350338027
DOIs	https://doi.org/10.1109/CICTN57981.2023.10141044
Publication status	Published - 7 Jun 2023
Externally published	Yes
Event	2023 International Conference on Computational Intelligence, Communication Technology and Networking - Ghaziabad, India Duration: 20 Apr 2023 → 21 Apr 2023 http://www.cictn.abes.ac.in/

Conference

Conference	2023 International Conference on Computational Intelligence, Communication Technology and Networking
Abbreviated title	CICTN
Country/Territory	India
City	Ghaziabad
Period	20/04/23 → 21/04/23
Internet address	http://www.cictn.abes.ac.in/

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10.1109/CICTN57981.2023.10141044

Cite this

@inproceedings{6efa66889b8c4e4d801c0fd551672d1a,

title = "Comparative Analysis of Speech Emotion Recognition Models and Technique",

abstract = "Speech Emotion Recognition (SER) refers to accurately predicting human emotions from their speech. The ability to predict emotions through speech signals is a motivating factor in achieving Human-Computer Interaction (HCI). This paper contains a comparative study of the existing research on speech emotion models. It makes use of the RAVDESS and SAVEE dataset containing audio input. The study of speech emotion recognition is made on SVM, CNN, KNN, MLP, Decision Tree, XGBoost, and Random Forest models. This paper presents a comparative analysis of the models highlighting the accuracy, F1 Score, bar plots, and loss graphs of the same. The paper also highlights the significant future areas for study in speech emotion recognition.",

author = "A. Agrawal and A. Jain and B. Kaur and S. Jangid and K. Kadian and V. Dwivedi and S. Garhwal",

year = "2023",

month = jun,

day = "7",

doi = "10.1109/CICTN57981.2023.10141044",

language = "English",

booktitle = "2023 International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023",

publisher = "IEEE",

note = "2023 International Conference on Computational Intelligence, Communication Technology and Networking, CICTN ; Conference date: 20-04-2023 Through 21-04-2023",

url = "http://www.cictn.abes.ac.in/",

}

Agrawal, A, Jain, A, Kaur, B, Jangid, S, Kadian, K, Dwivedi, V & Garhwal, S 2023, Comparative Analysis of Speech Emotion Recognition Models and Technique. in 2023 International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023. IEEE, 2023 International Conference on Computational Intelligence, Communication Technology and Networking, Ghaziabad, India, 20/04/23. https://doi.org/10.1109/CICTN57981.2023.10141044

TY - GEN

T1 - Comparative Analysis of Speech Emotion Recognition Models and Technique

AU - Agrawal, A.

AU - Jain, A.

AU - Kaur, B.

AU - Jangid, S.

AU - Kadian, K.

AU - Dwivedi, V.

AU - Garhwal, S.

PY - 2023/6/7

Y1 - 2023/6/7

N2 - Speech Emotion Recognition (SER) refers to accurately predicting human emotions from their speech. The ability to predict emotions through speech signals is a motivating factor in achieving Human-Computer Interaction (HCI). This paper contains a comparative study of the existing research on speech emotion models. It makes use of the RAVDESS and SAVEE dataset containing audio input. The study of speech emotion recognition is made on SVM, CNN, KNN, MLP, Decision Tree, XGBoost, and Random Forest models. This paper presents a comparative analysis of the models highlighting the accuracy, F1 Score, bar plots, and loss graphs of the same. The paper also highlights the significant future areas for study in speech emotion recognition.

AB - Speech Emotion Recognition (SER) refers to accurately predicting human emotions from their speech. The ability to predict emotions through speech signals is a motivating factor in achieving Human-Computer Interaction (HCI). This paper contains a comparative study of the existing research on speech emotion models. It makes use of the RAVDESS and SAVEE dataset containing audio input. The study of speech emotion recognition is made on SVM, CNN, KNN, MLP, Decision Tree, XGBoost, and Random Forest models. This paper presents a comparative analysis of the models highlighting the accuracy, F1 Score, bar plots, and loss graphs of the same. The paper also highlights the significant future areas for study in speech emotion recognition.

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85163191483&partnerID=MN8TOARS

U2 - 10.1109/CICTN57981.2023.10141044

DO - 10.1109/CICTN57981.2023.10141044

M3 - Conference contribution

BT - 2023 International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

PB - IEEE

T2 - 2023 International Conference on Computational Intelligence, Communication Technology and Networking

Y2 - 20 April 2023 through 21 April 2023

ER -

Comparative Analysis of Speech Emotion Recognition Models and Technique

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