Abstract
We propose to adapt a virtual agent called ‘Zara the Supergirl’ to user personality. User personality is deducted through two models, one based on raw audio and the other based on speech transcription text. Both models show good performance, with an average F-score of 69.6 for personality perception from audio, and an average F-score of 71.0 for recognition from text. Both models deploy a Convolutional Neural Network. Through a Human-Agent Interaction study we find correlations between user personality and preferred agent personality. The study suggests that especially the Openness user personality trait correlates with a stronger preference for agents with more gentle personality. People also sense more empathy and enjoy better conversations when agents adapt their personalities.
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References
Celli F, Lepri B, Biel JI, Gatica-Perez D, Riccardi G, Pianesi F (2014) The workshop on computational personality recognition 2014. In: Proceedings of the 22nd ACM international conference on Multimedia. ACM, pp 1245–1246
Digman JM (1990) Personality structure: emergence of the five-factor model. Annu Rev Psychol 41(1):417–440
Ivanov A, Chen X (2012) Modulation spectrum analysis for speaker personality trait recognition. In: INTERSPEECH, pp 278–281
Kalchbrenner N, Grefenstette E, Blunsom P (2014) A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188
Kiesler DJ (1983) The 1982 interpersonal circle: a taxonomy for complementarity in human transactions? Psychol Rev 90(3):185
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
Kingma P, Ba JL (2015) ADAM: a method for stochastic optimization. In: Conference paper at ICLR
Mikolov T et al (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst
Mohammadi G, Vinciarelli, A (2012) Automatic personality perception: prediction of trait attribution based on prosodic features. IEEE Trans Affect Comput 3(3). (July-September 2012)
Nass C, Moon Y, Fogg BJ, Reeves B, Dryer C (1995) Can computer personalities be human personalities? In: Conference companion on Human factors in computing systems, pp. 228–229
Palaz D et al (2015) Analysis of CNN-based speech recognition system using raw speech as input. In: INTERSPEECH
Ponce-Lpez V et al (2016) ChaLearn LAP 2016: first round challenge on first impressions-dataset and results. In: Hua G, Jgou H (eds) Computer vision ? ECCV 2016 Workshops, ECCV 2016. Lecture Notes in Computer Science, vol 9915. Springer, Cham
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929
Verhoeven B, Daelemans W, De Smedt T (2013) Ensemble methods for personality recognition. In: Proceedings of the workshop on computational personality recognition
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Kampman, O., Siddique, F.B., Yang, Y., Fung, P. (2019). Adapting a Virtual Agent to User Personality. In: Eskenazi, M., Devillers, L., Mariani, J. (eds) Advanced Social Interaction with Agents . Lecture Notes in Electrical Engineering, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-319-92108-2_13
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DOI: https://doi.org/10.1007/978-3-319-92108-2_13
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