Abstract
We propose a model for generating head nods from an utterance text considering personality traits. We have been investigating the automatic generation of body motion, such as nodding, from utterance text in dialog agent systems. Human body motion varies greatly depending on personality. Therefore, it is important to appropriately generate body motion according to the personality of the dialog agent. To construct our model, we first compiled a Japanese corpus of 24 dialogues including utterance, nod information, and personality traits (Big Five) of participants. Our nod-generation model also estimates the presence, frequency, and depth during each phrase by using various types of language information extracted from utterance text and personality traits. We evaluated how well the model can generate and estimate nods based on individual personality traits. The results indicate that our model using language information and personality trails outperformed a model using only language information.
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References
Amos B, Ludwiczuk B, Satyanarayanan M (2016) Openface: a general-purpose face recognition library with mobile applications. Technical report, CMU-CS-16-118, CMU School of Computer Science
Beskow J, Granstrom B, House D (2006) Visual correlates to prominence in several expressive modes. In: INTERSPEECH
BirdWhistell RL (1970) Kinesics and context. University of Pennsylvania Press
Busso C, Deng Z, Grimm M, Neumann U, Narayanan S (2007) Rigid head motion in expressive speech animation: analysis and synthesis. In: IEEE transactions on audio, speech, and language processing, pp 1075–1086
Fuchi T, Takagi S (1998) Japanese morphological analyzer using word cooccurrence -jtag. In: International conference on computational linguistics, pp 409–413
Graf HP, Cosatto E, Strom V, Huang FJ (2002) Visual prosody: facial movements accompanying speech. In: IEEE international conference on automatic face and gesture recognition, pp 381–386
Higashinaka R, Imamura K, Meguro T, Miyazaki C, Kobayashi N, Sugiyama H, Hirano T, Makino T, Matsuo Y (2014) Towards an open-domain conversational system fully based on natural language processing. In: International conference on computational linguistics, pp 928–939
Ishi CT, Haas J, Wilbers FP, Ishiguro H, Hagita N (2007) Analysis of head motions and speech, and head motion control in an android. In: IEEE/RSJ international conference on intelligent robots and systems, pp 548–553
Ishi CT, Ishiguro H, Hagita N (2010) Head motion during dialogue speech and nod timing control in humanoid robots. In: ACM/IEEE international conference on human-robot interaction, pp 293–300
Ishii R, Katayama T, Higashinaka R, Tomita J (2018) Automatic generation of head nods using utterance texts. In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 1143–1149
Ishii R, Higashinaka R, Nishida K, Katayama T, Kobayashi N, Tomita J (2018) Automatically generating head nods with linguistic information. In: Meiselwitz G (ed) Social computing and social media. Springer International Publishing, Cham, Technologies and analytics, pp 383–391
Ishii R, Katayama T, Higashinaka R, Tomita J (2018) Automatic generation system of virtual agent’s motion using natural language. In: Proceedings of the 18th international conference on intelligent virtual agents, IVA ’18, New York, NY, USA, 2018. ACM, pp 357–358
Ishii R, Katayama T, Higashinaka R, Tomita J (2018) Generating body motions using spoken language in dialogue. In: Intelligent virtual agents (IVA’18)
Iwano Y, Kageyama S, Morikawa E, Nakazato S, Shirai K (1996) Analysis of head movements and its role in spoken dialogue. In: International conference on spoken language, pp 2167–2170
Munhall KG, Jones JA, Callan DE, Kuratate T, Vatikiotis-Bateson E (2004) Visual prosody and speech intelligibility: head movement improves auditory speech perception 15(2):133–137
Koiso H, Horiuchi Y, Tutiya S, Ichikawa A, Den Y (1998) An analysis of turn-taking and backchannels based on prosodic and syntactic features in japanese map task dialogs. Lang Speech 41:295–321
Lohse M, Rothuis R, Gallego-Pérez J, Karreman DE, Evers V (2014) Robot gestures make difficult tasks easier: the impact of gestures on perceived workload and task performance. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’14, New York, NY, USA, 2014. ACM, pp 1459–1466
McBreen HM, Jack MA (2001) Evaluating humanoid synthetic agents in e-retail applications. IEEE Trans Syst, Man, Cybern - Part A: Syst Humans 31:5
Meguro T, Higashinaka R, Minami Y, Dohsaka K (2010) Controlling listening-oriented dialogue using partially observable markov decision processes. In: International conference on computational linguistics, pp 761–769
Quinlan JR (1996) Improved use of continuous attributes in c4.5. J Artif Intell Res 4:77–90
Watanabe T, Danbara R, Okubo M (2003) Effects of a speech-driven embodied interactive actor interactor on talker’s speech characteristics. In: IEEE international workshop on robot-human interactive communication, pp 211–216
Wittenburg P, Brugman H, Russel A, Klassmann A, Sloetjes H (2006) Elan a professional framework for multimodality research. In: International conference on language resources and evaluation
Yehia HC, Kuratate T, Vatikiotis-Bateson E (2002) Linking facial animation, head motion and speech acoustics 30(3):555–568
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Ishii, R., Katayama, T., Higashinaka, R., Tomita, J. (2021). Automatic Head-Nod Generation Using Utterance Text Considering Personality Traits. In: Marchi, E., Siniscalchi, S.M., Cumani, S., Salerno, V.M., Li, H. (eds) Increasing Naturalness and Flexibility in Spoken Dialogue Interaction. Lecture Notes in Electrical Engineering, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-15-9323-9_26
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DOI: https://doi.org/10.1007/978-981-15-9323-9_26
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