poster

Emotion Recognition from Speech: A Classroom Experiment

Authors:

Rozalia Nikopoulou,

Ioannis Vernikos,

Evaggelos Spyrou,

Phivos MylonasAuthors Info & Claims

PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference

Pages 104 - 105

https://doi.org/10.1145/3197768.3197782

Published: 26 June 2018 Publication History

Get Access

Abstract

In this position paper we present an approach for the recognition of emotions from speech. Our goal is to understand the affective state of learners upon a learning process. We propose an approach that uses visual representations of the spectrum of audio segments, which are classified using the Bag-of-Visual Words model. Our approach is applied on a real-life dataset that contains interviews from middle-school students, collected upon a classroom experiment.

References

[1]

C.N. Anagnostopoulos, T. Iliou and I. Giannoukos, Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011, Artificial Intelligence Review, 43(2), pp. 155--177, 2015.

Digital Library

Google Scholar

[2]

K. Bahreini, R. Nadolski and W. Westera. Towards real-time speech emotion recognition for affective e-learning. Educ. and Inform. Techn. 21(5):1367--86, 2016.

Crossref

Google Scholar

[3]

H. Bay, A. Ess, T. Tuytelaars and L. Van Gool, Speeded-up robust features (SURF), Computer Vision and Image Understanding, 110(3), pp. 346--359, 2008.

Digital Library

Google Scholar

[4]

T. Giannakopoulos, A. Pikrakis and S. Theodoridis, A dimensional approach to emotion recognition of speech from movies. In IEEE Int'l Conf. Acoustics, Speech and Signal Processing (ICASSP), 2009.

Digital Library

Google Scholar

[5]

T. Giannakopoulos, pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis, PloS one, vol. 10(2), pp. e0144610, 2015.

Google Scholar

[6]

M. Grimm, K. Kroschel, E. Mower and S. Narayanan, Primitives-based evaluation and estimation of emotions in speech. Speech Comm., 49(10), pp. 787--800, 2007.

Digital Library

Google Scholar

[7]

J.G. MartiÃ&eng;nez, Recognition and emotions. A critical approach on education, Procedia-Social and Behavioral Sciences 46 pp. 3925--3930, 2012.

Crossref

Google Scholar

[8]

MATLAB and Computer Vision Toolbox Release 2016a, The MathWorks, Inc., Natick, Massachusetts, United States.

Google Scholar

[9]

A. Nogueiras, A. Moreno, A. Bonafonte and J.B. Marino, Speech emotion recognition using hidden Markov models. In INTERSPEECH, 2001.

Google Scholar

[10]

M. Papakostas, E. Spyrou, T. Giannakopoulos, G. Siantikos, D. Sgouropoulos, Ph. Mylonas and F. Makedon. Deep Visual Attributes vs. Hand-Crafted Audio Features on Multidomain Speech Emotion Recognition, Computation 5(2), 26, MDPI, 2017.

Crossref

Google Scholar

[11]

E. Spyrou, T. Giannakopoulos, D. Sgouropoulos and M. Papakostas, Extracting Emotions from Speech using a Bag-of-Visual-Words Approach. Int'l Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), 2017.

Google Scholar

[12]

A. Tickle, S. Raghu and M. Elshaw. Emotional recognition from the speech signal for a virtual education agent. Journal of Physics: Conference Series 2013 (Vol. 450, No. 1, p. 012053). IOP Publishing, 2013.

Google Scholar

[13]

Y. Wang and L. Guan, Recognizing human emotional state from audiovisual signals, IEEE Trans. on Multimedia, 10(5), pp. 936--946, 2008.

Digital Library

Google Scholar

Cited By

View all

Beccaro WArjona Ramírez MLiaw WGuimarães H(2024)Analysis of Oral Exams With Speaker Diarization and Speech Emotion Recognition: A Case StudyIEEE Transactions on Education10.1109/TE.2023.332115567:1(74-86)Online publication date: Feb-2024
https://doi.org/10.1109/TE.2023.3321155
Surana ARathod MGite SPatil SKotecha KSelvachandran GQuek SAbraham A(2023)An audio-based anger detection algorithm using a hybrid artificial neural network and fuzzy logic modelMultimedia Tools and Applications10.1007/s11042-023-16815-783:13(38909-38929)Online publication date: 6-Oct-2023
https://doi.org/10.1007/s11042-023-16815-7
Wang GGong CWang S(2022)A Review of Automatic Detection of Learner States in Four Typical Learning ScenariosAdaptive Instructional Systems10.1007/978-3-031-05887-5_5(53-72)Online publication date: 16-Jun-2022
https://doi.org/10.1007/978-3-031-05887-5_5
Show More Cited By

Recommendations

Application of Emotion Recognition and Modification for Emotional Telugu Speech Recognition
Abstract
Majority of the automatic speech recognition systems (ASR) are trained with neutral speech and the performance of these systems are affected due to the presence of emotional content in the speech. The recognition of these emotions in human speech ...
Emotion recognition from speech: a review

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on ...
Speech emotion recognition based on an improved brain emotion learning model
Abstract
Human-robot emotional interaction has developed rapidly in recent years, in which speech emotion recognition plays a significant role. In this paper, a speech emotion recognition method based on an improved brain emotional learning (BEL) model is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

PETRA '18: Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference

June 2018

591 pages

ISBN:9781450363907

DOI:10.1145/3197768

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

NSF: National Science Foundation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 June 2018

Check for updates

Author Tags

Qualifiers

Poster
Research
Refereed limited

Conference

PETRA '18

PETRA '18: The 11th PErvasive Technologies Related to Assistive Environments Conference

June 26 - 29, 2018

Corfu, Greece

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
154
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Beccaro WArjona Ramírez MLiaw WGuimarães H(2024)Analysis of Oral Exams With Speaker Diarization and Speech Emotion Recognition: A Case StudyIEEE Transactions on Education10.1109/TE.2023.332115567:1(74-86)Online publication date: Feb-2024
https://doi.org/10.1109/TE.2023.3321155
Surana ARathod MGite SPatil SKotecha KSelvachandran GQuek SAbraham A(2023)An audio-based anger detection algorithm using a hybrid artificial neural network and fuzzy logic modelMultimedia Tools and Applications10.1007/s11042-023-16815-783:13(38909-38929)Online publication date: 6-Oct-2023
https://doi.org/10.1007/s11042-023-16815-7
Wang GGong CWang S(2022)A Review of Automatic Detection of Learner States in Four Typical Learning ScenariosAdaptive Instructional Systems10.1007/978-3-031-05887-5_5(53-72)Online publication date: 16-Jun-2022
https://doi.org/10.1007/978-3-031-05887-5_5
Arya RPandey DKalia AZachariah BSandhu IAbrol D(2021)Speech based Emotion Recognition using Machine Learning2021 IEEE Mysore Sub Section International Conference (MysuruCon)10.1109/MysuruCon52639.2021.9641642(613-617)Online publication date: 24-Oct-2021
https://doi.org/10.1109/MysuruCon52639.2021.9641642
Lieskovska EJakubec MJarina R(2020)Speech Emotion Recognition Overview and Experimental Results2020 18th International Conference on Emerging eLearning Technologies and Applications (ICETA)10.1109/ICETA51985.2020.9379218(388-393)Online publication date: 12-Nov-2020
https://doi.org/10.1109/ICETA51985.2020.9379218

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Application of Emotion Recognition and Modification for Emotional Telugu Speech Recognition

Emotion recognition from speech: a review

Speech emotion recognition based on an improved brain emotion learning model