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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