Generating Body Motions using Spoken Language in Dialogue

We propose a model to automatically generate whole body motions accompanying utterances at appropriate times, similar to humans, by using various types of natural-language-analysis information obtained from spoken language. Specifically, we focus on the co-occurrence relationship between various types of natural-language-analysis information such as words included in the spoken language, parts of speech, a thesaurus, word positions, dialogue acts of the spoken language, and human motions. Our model automatically generates nods, head postures, facial expressions, hand gestures, and upper-body posture using such information. We first recorded a two-person dialogue and constructed a multimodal corpus including utterance and whole body motion information. Next, using the constructed corpus, we constructed our model for generating a motion for each phrase unit using machine learning and using words, parts of speech, a thesaurus, word positions, and speech acts of the entire spoken language as inputs. These types of natural-language-analysis information were useful for motion generation. The effectiveness of our model was verified through a subjective experiment using a virtual conversational agent. As a result, the agent's body motions and impressions regarding naturalness of motion, degree of coincidence between utterance and motion, humanness of the agent, and likability of the agent improved with our model.