Abstract
This paper proposes a method for sensitivity communication robots which infer their dialogist’s emotion. The method is based on the Bayesian approach: by using a Bayesian modeling for prosodic features. In this research, we focus the elements of emotion included in dialogist’s voice. Thus, as training datasets for learning Bayesian networks, we extract prosodic feature quantities from emotionally expressive voice data. Our method learns the dependence and its strength between dialogist’s utterance and his emotion, by building Bayesian networks. Bayesian information criterion, one of the information theoretical model selection method, is used in the building Bayesian networks. The paper finally proposes a reasoner to infer dialogist’s emotion by using a Bayesian network for prosodic features of the dialogist’s voice. The paper also reports some empirical reasoning performance.
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References
Akiba, T., Tanaka, H.: A Bayesian approach for user modelling in dialog systems. In: 15th International Conference of Computational Linguistics, pp. 1212–1218 (1994)
Cooper, G.F., Herskovits, E.: A Bayesian method for constructing Bayesian belief networks from databases, pp. 86–94 (1991)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18(1), 32–80 (2001)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royals Statistical Society B 39, 1–38 (1977)
Endo, G., Nakanishi, J., Morimoto, J., Cheng, G.: Experimental studies of a neural oscillator for biped locomotion with QRIO. In: IEEE International Conference on Robotics and Automation (ICRA 2005), pp. 598–604 (2005)
Fujita, M.: Development of an Autonomous Quadruped Robot for Robot Entertainment. Autonomous Robots 5, 7–18 (1998)
Fujita, M., Kitano, H., Doi, T.: Robot Entertainment, ch. 2. In: Druin, A., Hendler, J. (eds.) Robots for kids: exploring new technologies for learning, pp. 37–70. Morgan Kaufmann, San Francisco (2000)
Henrion, M.: Propagating uncertainty in Bayesian networks by logic sampling. Uncertainty in Artificial Intelligence 2, 149–163 (1988)
Jensen, F.V.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001)
Kanda, S., Murase, Y., Fujioka, K.: Internet-based Robot: Mobile Agent Robot of Next-generation (MARON-1), vol. 54, pp. 285–292 (2003) (in Japanese)
Kanoh, M., Kato, S., Itoh, H.: Facial expressions using emotional space in sensitivity communication robot “ifbot”. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), pp. 1586–1591 (2004)
Sjölander, K.: The Snack Sound Toolkit, http://www.speech.kth.se/snack/
Kato, S., Ohsiro, S., Watabe, K., Itoh, H., Kimura, K.: A domestic robot with sensitive communication and its vision system for talker distinction. Intelligent Autonomous Systems 8, 1162–1168 (2004)
Kato, S., Ohshiro, S., Itoh, H., Kimura, K.: Development of a communication robot ifbot. In: IEEE International Conference on Robotics and Automation (ICRA 2004), pp. 697–702 (2004)
Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence. Chapman & Hall/CRC, Boca Raton (2003)
Business Design Laboratory Co. Ltd. The Extremely Expressive Communication Robot, Ifbot, http://www.business-design.co.jp/en/product/001/index.html
Murase, Y., Yasukawa, Y., Sakai, K., et al.: Design of a compact humanoid robot as a platform (in Japanese). In: Proc. of the 19-th conf. of Robotics Society of Japan, Japan, pp. 789–790 (2001), http://pr.fujitsu.com/en/news/2001/09/10.html
Murphy, K.P.: Bayes Net Toolbox, http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html
Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: an empirical study, 467–475 (1999)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)
Scherer, K.R., Johnstone, T., Klasmeyer, G.: Vocal expression of emotion. In: Davidson, R.J., Goldsmith, H., Scherer, K.R. (eds.) Handbook of the Affective Sciences, pp. 433–456. Oxford University Press, Oxford (2003)
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Kato, S., Sugino, Y., Itoh, H. (2006). A Bayesian Approach to Emotion Detection in Dialogist’s Voice for Human Robot Interaction. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_123
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DOI: https://doi.org/10.1007/11893004_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
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