Abstract
People propose various ideas and opinions in public organization and conferences. In order to reach an agreement among the participants, discussion is essential. During a discussion, difference in the processing of forming an agreement will affect the conclusion. So major statements should be analyzed for building a consensus. However, in the process of forming consensus, a proper understanding of the statements acting as the basis among various discussants, is necessary. In this study, the relationship between the consciousness of the conversation and the state of support is discovered through the speech emotional perception of the conversation. Also, the state of support—supportive, negative and unknown, are inferred. In addition to listening experiments on the emotions and support states, the application of the analysis of the speech emotion recognition process is discussed on the basis of verifying the emotion of speech and the dependence of the support state. The accuracy of the average objective recognition rate can be increased to 75% in the formation of the consensus during speech conversation.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Farnham, S., Chesley, H.R., McGhee, D.E., Kawal, R., Landau, J.: Structured online interactions: improving the decision making of small discussion groups. In: Proceedings of CSCW (2000)
Scott, S.L. Comparing consensus Monte Carlo strategies for distributed Bayesian computation. Brazilian Journal of Probability and Statistics. (2017)
Scott, S.L., Blocker, A.W., Bonassi, F.V., Chipman, H.A., George, E.I., McCulloch, R.E.: Bayes and big data: the consensus monte carlo algorithm. In: Bayes 250 (2013). http://research.google.com/pubs/pub41849.html
Ko, A.J., Chilana, P.K.: Design, discussion, and dissent in open bug reports. In: Proceedings of the iConference (2011)
Moghaddam, R.Z., Bailey, B., Poon, C.: IdeaTracker: an interactive visualization supporting collaboration and consensus building in online interface design discussions. In: Proceedings of INTERACT (2011)
Albino, N., Asunción M., Antonio B., José B.: Speech Emotion Recognition Using Hidden Markov Models Eurospeech—Scandinavia (2001)
Chennoukh, S., Gerrits, A., Miet, G., Sluijter, R.: Speech enhancement via frequency extension using spectral frequency. Proceedings of the ICASSP, Salt Lake City, vol. 5 (2001)
Acknowledgements
Supported by a grant from Natural science fund for colleges and universities in Jiangsu Province (No. 17KBJ520002) and Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) under Grant No. PPZY2015A090.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
He, N., Liu, Y. (2019). An Application of Support States from Speech Emotions in Consensus Building. In: Pan, JS., Lin, JW., Sui, B., Tseng, SP. (eds) Genetic and Evolutionary Computing. ICGEC 2018. Advances in Intelligent Systems and Computing, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-13-5841-8_34
Download citation
DOI: https://doi.org/10.1007/978-981-13-5841-8_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5840-1
Online ISBN: 978-981-13-5841-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)