Abstract
With an increasing number of AI applications in everyday life, the discussion on how to improve the usability of AI or robot devices has been taking place. This is particularly true for Socially Assistive Robots (SAR). The presented study in this paper is an attempt to identify speech characteristics such as sound and lexical features among different personality groups, specifically per personality dimension of Myers-Briggs Type Indicator (MBTI), and to design an Artificial Neural Network reflecting the correlations found between speech characteristics and personality traits. The current study primarily reports the relationship between various speech characteristics (both mechanical and lexical) and personality dimensions identified in MBTI. Based on significant findings, an ANN (Artificial Neural Network) has been designed in an effort to predict personality traits only from speech processing. The current model yielded 75% accuracy in its predictive ability, warranting further attention to the applicability of speech data in developing and improving various domains of human-computer interactions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andrist, S., Mutlu, B., Tapus, A.: Look like me: matching robot personality via gaze to increase motivation. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea, 18–23 April 2015, pp. 3603–3612 (2015)
Boersma, P., Weenink, D.: Praat: doing phonetics by computer [Computer program]. Version 6.1.26 (2020). Accessed 5 Oct 2020. http://www.praat.org/
Goetz, J., Kiesler, S., Powers, A.: Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: Proceedings of the IEEE International Workshop on Robot and Human Interactive Communication, Millbrae, CA, USA, 2 November 2003, pp. 55–60 (2003)
Kang, K.I., Freedman, S., Mataric, M.J., Cunningham, M.J., Lopez, B.: A hands-off physical therapy assistance robot for cardiac patients. In: Proceedings of the 9th IEEE International Conference on Rehabilitation Robotics, Chicago, IL, USA, 28 June–1 July 2005, pp. 337–340 (2005)
Nass, C., Jonsson, I.M., Harris, H., Reaves, B., Endo, J., Brave, S., Takayama, L.: Improving automotive safety by pairing driver emotion and car voice emotion. In: Proceedings of the CHI 2005 Extended Abstracts on Human Factors in Computing Systems, Portland, OR, USA, 2–7 April 2005, pp. 1973–1976 (2005)
Park, J., Lee, S., Brotherton, K., Um, D., Park, J.: Identification of speech characteristics to distinguish human personality of introversive and extroversive male groups. Int. J. Environ. Res. Public Health 17, 21–25 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lee, S., Um, D., Park, J. (2021). Socio-Cognitive Interaction Between Human and Computer/Robot for HCI 3.0. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-68452-5_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68451-8
Online ISBN: 978-3-030-68452-5
eBook Packages: Computer ScienceComputer Science (R0)