Virtual assistant: enhancing content acquisition by eliciting information from humans
Multimedia Tools and Applications
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Aiming at facilitating multi-party conversations in a shared-virtual-space voice chat environment, we propose an avatar’s gaze behavior model for turn-taking in multi-party conversations, and a shared-virtual-space voice chat system with automatic avatar gaze direction control function using user utterance information. The use of the utterance information attained easy-to-use automatic gaze control without eye-tracking camera or manual operation. In our gaze behavior model, a conversation was divided into three states: during-utterance, right-after-utterance, and silence. For each state, avatar’s gaze behaviors are controlled based on a probabilistic state transition model. Previous studies reveled that gaze has a power of selecting the next speaker and urge her/him to speak, and continuous gaze has a risk of giving intimidating impression to the listener. Although explicit look-away from the conversational partner generally means interest to others, such gaze behaviors seem to help the speaker avoid threatening the listener’s face. In order to express less-face-threatening eye-gaze in virtual space avatars, our model introduces vague-gaze: the avatar looks at five degrees lower than the user’s eye position. Thus, in during-utterance state, the avatars were controlled using a probabilistic state transition model that transits among three states: eye contact, vague-gaze and look-away. It is expected that the vague-gaze reduces intimidating impression as well as facilitates conversational turn-taking. In right-after-utterance state, the speaker avatar keeps an eye contact for a few seconds to urge the next speaker to start a new turn. This is based on an observation of real face-to-face conversation. Finally, in silent state, avatar’s gaze direction is randomly changed to avoid giving intimidating impression. In our evaluation experiment, twelve subjects were divided into four groups, and requested to chat with the avatars and answer impressions for them using Likert scale. As for the during-utterance state, in terms of naturalness, intimidating impression reduction and turn-taking facilitation, a transition model consisting of vague-gaze and look-away was significantly effective, compared to the vague-gaze alone, the look-away alone and the fixed-gaze alone models . In the right-after-utterance state, any of the gaze control methods were significantly effective in facilitating turn-taking, compared to the fixed-gaze method. The evaluation experiment demonstrated the effectiveness of our avatar’s gaze control mechanism, and suggested that the gaze control based on the user utterance facilitates multi-party conversations in a virtual-space voice chat system.