SER: Speech Emotion Recognition Application Based on Extreme Learning Machine

Ainurrochman; Irfanur Ilham Febriansyah; Umi Laili Yuhana

doi:10.1109/ICTS52701.2021.9609016

SER: Speech Emotion Recognition Application Based on Extreme Learning Machine

Ainurrochman, Irfanur Ilham Febriansyah, Umi Laili Yuhana

Institut Teknologi Sepuluh Nopember

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

4 Citations (Scopus)

Abstract

Nowadays, device control is commonly using the human body feature or voice recognition technology. To expand the functionality of voice recognition, plenty of researchers have developed speech emotion recognition. By recognizing sound emotions, a system can provide better and beneficial decision-making output. This paper describes the development of an application that is able to recognize speech emotions using Extreme Learning Machine (ELM). We use the dataset from Toronto Emotional Speech Set (TESS). The dataset contains 2800 data points (audio files) in total and has high quality audio that focused on female voices to ensure the reliability of the data. The Speech Emotion Recognition application was design as web-based application that used Golang and Python which built with Extreme Learning Machine and Random Forest to recognize speech emotions. As a result, the functionality test shows that the application was able to satisfy 6 out of 6 requirements, and the accuracy test shows an accuracy value of 100% by identifying 70 out of 70 test data.

Original language	English
Title of host publication	Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	179-183
Number of pages	5
ISBN (Electronic)	9781665440592
DOIs	https://doi.org/10.1109/ICTS52701.2021.9609016
Publication status	Published - 2021
Event	13th International Conference on Information and Communication Technology and System, ICTS 2021 - Virtual, Online, Indonesia Duration: 20 Oct 2021 → 21 Oct 2021

Publication series

Name	Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021

Conference

Conference	13th International Conference on Information and Communication Technology and System, ICTS 2021
Country/Territory	Indonesia
City	Virtual, Online
Period	20/10/21 → 21/10/21

Keywords

Design
Golang
Phyton
Random Forest
Speech Emotion Recognition
Toronto Emotional Speech Set

Access to Document

10.1109/ICTS52701.2021.9609016

Cite this

Ainurrochman, Febriansyah, I. I., & Yuhana, U. L. (2021). SER: Speech Emotion Recognition Application Based on Extreme Learning Machine. In Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021 (pp. 179-183). (Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTS52701.2021.9609016

Ainurrochman ; Febriansyah, Irfanur Ilham ; Yuhana, Umi Laili. / SER : Speech Emotion Recognition Application Based on Extreme Learning Machine. Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 179-183 (Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021).

@inproceedings{58766ed74ffd4d478dd557a552f64893,

title = "SER: Speech Emotion Recognition Application Based on Extreme Learning Machine",

abstract = "Nowadays, device control is commonly using the human body feature or voice recognition technology. To expand the functionality of voice recognition, plenty of researchers have developed speech emotion recognition. By recognizing sound emotions, a system can provide better and beneficial decision-making output. This paper describes the development of an application that is able to recognize speech emotions using Extreme Learning Machine (ELM). We use the dataset from Toronto Emotional Speech Set (TESS). The dataset contains 2800 data points (audio files) in total and has high quality audio that focused on female voices to ensure the reliability of the data. The Speech Emotion Recognition application was design as web-based application that used Golang and Python which built with Extreme Learning Machine and Random Forest to recognize speech emotions. As a result, the functionality test shows that the application was able to satisfy 6 out of 6 requirements, and the accuracy test shows an accuracy value of 100% by identifying 70 out of 70 test data.",

keywords = "Design, Golang, Phyton, Random Forest, Speech Emotion Recognition, Toronto Emotional Speech Set",

author = "Ainurrochman and Febriansyah, {Irfanur Ilham} and Yuhana, {Umi Laili}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th International Conference on Information and Communication Technology and System, ICTS 2021 ; Conference date: 20-10-2021 Through 21-10-2021",

year = "2021",

doi = "10.1109/ICTS52701.2021.9609016",

language = "English",

series = "Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021",

address = "United States",

}

Ainurrochman, Febriansyah, II & Yuhana, UL 2021, SER: Speech Emotion Recognition Application Based on Extreme Learning Machine. in Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021. Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021, Institute of Electrical and Electronics Engineers Inc., pp. 179-183, 13th International Conference on Information and Communication Technology and System, ICTS 2021, Virtual, Online, Indonesia, 20/10/21. https://doi.org/10.1109/ICTS52701.2021.9609016

SER: Speech Emotion Recognition Application Based on Extreme Learning Machine. / Ainurrochman; Febriansyah, Irfanur Ilham; Yuhana, Umi Laili.
Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 179-183 (Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021).

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

TY - GEN

T1 - SER

T2 - 13th International Conference on Information and Communication Technology and System, ICTS 2021

AU - Ainurrochman,

AU - Febriansyah, Irfanur Ilham

AU - Yuhana, Umi Laili

PY - 2021

Y1 - 2021

N2 - Nowadays, device control is commonly using the human body feature or voice recognition technology. To expand the functionality of voice recognition, plenty of researchers have developed speech emotion recognition. By recognizing sound emotions, a system can provide better and beneficial decision-making output. This paper describes the development of an application that is able to recognize speech emotions using Extreme Learning Machine (ELM). We use the dataset from Toronto Emotional Speech Set (TESS). The dataset contains 2800 data points (audio files) in total and has high quality audio that focused on female voices to ensure the reliability of the data. The Speech Emotion Recognition application was design as web-based application that used Golang and Python which built with Extreme Learning Machine and Random Forest to recognize speech emotions. As a result, the functionality test shows that the application was able to satisfy 6 out of 6 requirements, and the accuracy test shows an accuracy value of 100% by identifying 70 out of 70 test data.

AB - Nowadays, device control is commonly using the human body feature or voice recognition technology. To expand the functionality of voice recognition, plenty of researchers have developed speech emotion recognition. By recognizing sound emotions, a system can provide better and beneficial decision-making output. This paper describes the development of an application that is able to recognize speech emotions using Extreme Learning Machine (ELM). We use the dataset from Toronto Emotional Speech Set (TESS). The dataset contains 2800 data points (audio files) in total and has high quality audio that focused on female voices to ensure the reliability of the data. The Speech Emotion Recognition application was design as web-based application that used Golang and Python which built with Extreme Learning Machine and Random Forest to recognize speech emotions. As a result, the functionality test shows that the application was able to satisfy 6 out of 6 requirements, and the accuracy test shows an accuracy value of 100% by identifying 70 out of 70 test data.

KW - Design

KW - Golang

KW - Phyton

KW - Random Forest

KW - Speech Emotion Recognition

KW - Toronto Emotional Speech Set

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U2 - 10.1109/ICTS52701.2021.9609016

DO - 10.1109/ICTS52701.2021.9609016

M3 - Conference contribution

AN - SCOPUS:85123305931

T3 - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021

SP - 179

EP - 183

BT - Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 20 October 2021 through 21 October 2021

ER -

Ainurrochman, Febriansyah II, Yuhana UL. SER: Speech Emotion Recognition Application Based on Extreme Learning Machine. In Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 179-183. (Proceedings of 2021 13th International Conference on Information and Communication Technology and System, ICTS 2021). doi: 10.1109/ICTS52701.2021.9609016

SER: Speech Emotion Recognition Application Based on Extreme Learning Machine

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Keywords

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