research-article

EmoReSp: an online emotion recognizer based on speech

Authors:

Pascal Wiggers,

Leon J. M. RothkrantzAuthors Info & Claims

CompSysTech '10: Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies

Pages 287 - 292

https://doi.org/10.1145/1839379.1839430

Published: 17 June 2010 Publication History

Abstract

The paper describes the development of an online real-time system able to recognize emotions from speech. A prosodic feature set was extracted from four databases of emotional speech (three with acted emotions and one with spontaneous ones). Two models were trained using support vector machines (SVM) or merged databases, for the purpose of providing a larger range of examples to the classifier and making it more general. The system outputs probabilities of a closed set of emotions and provides a time track of the emotions recognized in the valence and arousal continuum.

References

[1]

P. Boersma. Praat, a System for Doing Phonetics by Computer. Glot International, 5(9/10):341--345, 2001.

[2]

F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier, and B. Weiss. A Database of German Emotional Speech. Proceedings of Interspeech, pages 1517--1520, 2005.

[3]

C. C. Chang and C. J. Lin. LIBSVM: a Library for Support Vector Machines, 2001.

[4]

I. S. Engberg and A. V. Hansen. Documentation of the Danish Emotional Speech Database (DES). Internal AAU report, Center for Person Kommunikation, 1996.

[5]

F. Eyben, M. Wöllmer, A. Graves, B. Schuller, E. Douglas-Cowie and R. Cowie. On-line Emotion Recognition in a 3-D Activation-valence-time Continuum Using Acoustic and Linguistic cues. In Journal on Multimodal User Interfaces, 2010.

[6]

F. Eyben, M. Wöllmer, and B. Schuller. OpenEAR---Introducing the Munich Open-source Emotion and Affect Recognition Toolkit, in Proceedings of the Affective Computing and Intelligent Interaction, IEEE, Amsterdam, The Netherlands, 2009.

[7]

P. N. Juslin and K. R. Scherer. In J. Harrigan, R. Rosenthal, and K. Scherer, (Eds.) - The New Handbook of Methods in Nonverbal Behavior Research, chapter Vocal Expression of Affect, pages 65--135. Oxford University Press, 2005.

[8]

S. Kim, P. Georgiou, S. Lee, and S. Narayanan. Real-time Emotion Detection System Using Speech: Multi-modal Fusion of Different Timescale Features", Proceedings of IEEE Multimedia Signal Processing Workshop, Chania, Greece, 2007.

[9]

I. Lefter, L. J. M Rothkrantz, P. Wiggers, and D. A. van Leeuwen. Automatic Stress Detection in Emergency (Telephone) Calls. International Journal on Intelligent Defense Support Systems, 2010. submitted.

[10]

I. Lefter, L. J. M Rothkrantz, P. Wiggers, and D. A. van Leeuwen - Emotion Recognition from Speech by Combining Databases and Fusion of Classifiers, Text, Speech and Dialogue, 2010. submitted.

Digital Library

[11]

O. Martin, I. Kotsia, B. Macq, and I. Pitas. The eNTERFACE'05 Audio-Visual Emotion Database. 22nd International Conference on Data Engineering Workshops, 2006.

Digital Library

[12]

M. Pantic and L. J. M. Rothkrantz. Towards an Affect-sensitive Multimodal Human-Computer Interaction. Proceedings of the IEEE, pages 1370--1390, 2003.

[13]

M. Shami and W. Verhelst. Automatic Classification of Expressiveness in speech: A Multi-corpus Study. Speaker Classification II: Selected Projects, pages 43--56, 2007.

Digital Library

[14]

S. Steidl. Automatic Classification of Emotion-Related User States in Spontaneous Children's Speech. Logos Verlag, 1 edition, 2009.

[15]

K. P. Truong and S. Raaijmakers. Automatic Recognition of Spontaneous Emotions in Speech Using Acoustic and Lexical Features. In Proceedings of the 5^th international workshop on Machine Learning for Multimodal Interaction (MLMI), pages 161--172. Springer-Verlag, 2008.

Digital Library

[16]

T. Vogt, E. Andre, and N. Bee. EmoVoice - a framework for online recognition of emotions from voice. In Proc. IEEE PIT 2008, volume 5078 of LNCS, pages 188--199. Springer, June 2008.

Digital Library

[17]

Whissell, C. M. The dictionary of affect in language. In Robert Plutchik and Henry Kellerman (Ed.), Emotion: Theory, Research, and Experience (pp. 113--131). New York: Academic Press, 1989.

Cited By

Walther M(2024)An AI-Based Framework for Speech and Voice Analytics to Automatically Assess the Quality of Service ConversationsArtificial intelligence in application10.1007/978-3-658-43843-2_11(175-192)Online publication date: 11-Jul-2024
https://doi.org/10.1007/978-3-658-43843-2_11
Wang FSahli HGao JJiang DVerhelst W(2015)Relevance units machine based dimensional and continuous speech emotion predictionMultimedia Tools and Applications10.1007/s11042-014-2319-174:22(9983-10000)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1007/s11042-014-2319-1
Schuller BBatliner ASteidl SSeppi D(2011)Recognising realistic emotions and affect in speechSpeech Communication10.1016/j.specom.2011.01.01153:9-10(1062-1087)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1016/j.specom.2011.01.011

Recommendations

Survey on speech emotion recognition: Features, classification schemes, and databases

Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion ...
On the perception of "segmental intonation": F0 context effects on sibilant identification in German

In normal modally voiced utterances, voiceless fricatives like [s], [ź], [f], and [x] vary such that their aperiodic pitch impressions mirror the pitch level of the adjacent F0 contour. For instance, if the F0 contour creates a high or low pitch context,...
Modeling Emotion and Attitude in Speech by Means of Perceptually Based Parameter Values

This study focuses on the perception of emotion and attitude in speech. The ability to identify vocal expressions of emotion and/or attitude in speech material was investigated. Systematic perception experiments were carried out to determine optimal ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

CompSysTech '10: Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies

June 2010

575 pages

ISBN:9781450302432

DOI:10.1145/1839379

Editors:
Boris Rachev
Varna Technical University, Bulgaria
,
Angel Smrikarov
University of Ruse, Ruse, Bulgaria

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CompSysTech '10

CompSysTech '10: Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing

June 17 - 18, 2010

Sofia, Bulgaria

Acceptance Rates

Overall Acceptance Rate 241 of 492 submissions, 49%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
287
Total Downloads

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

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Walther M(2024)An AI-Based Framework for Speech and Voice Analytics to Automatically Assess the Quality of Service ConversationsArtificial intelligence in application10.1007/978-3-658-43843-2_11(175-192)Online publication date: 11-Jul-2024
https://doi.org/10.1007/978-3-658-43843-2_11
Wang FSahli HGao JJiang DVerhelst W(2015)Relevance units machine based dimensional and continuous speech emotion predictionMultimedia Tools and Applications10.1007/s11042-014-2319-174:22(9983-10000)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1007/s11042-014-2319-1
Schuller BBatliner ASteidl SSeppi D(2011)Recognising realistic emotions and affect in speechSpeech Communication10.1016/j.specom.2011.01.01153:9-10(1062-1087)Online publication date: 1-Nov-2011
https://dl.acm.org/doi/10.1016/j.specom.2011.01.011

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten