An Application of Support States from Speech Emotions in Consensus Building

  • Ning HeEmail author
  • Yang Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 834)


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.


Emotion recognition MFCC Consensus building Support state 



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.

Ethical Approval

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.


  1. 1.
    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)Google Scholar
  2. 2.
    Scott, S.L. Comparing consensus Monte Carlo strategies for distributed Bayesian computation. Brazilian Journal of Probability and Statistics. (2017)Google Scholar
  3. 3.
    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).
  4. 4.
    Ko, A.J., Chilana, P.K.: Design, discussion, and dissent in open bug reports. In: Proceedings of the iConference (2011)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Albino, N., Asunción M., Antonio B., José B.: Speech Emotion Recognition Using Hidden Markov Models Eurospeech—Scandinavia (2001)Google Scholar
  7. 7.
    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)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Changzhou College of Information TechnologyWujin District, ChangzhouChina

Personalised recommendations