[4N1-IS-3a] Agents (1/2)

[4N1-IS-3a-03] Automatic Utterance Selection Based on Prosodic Features of Children's Vocalizations

〇Yuki Kubo¹, Natsuki Oka¹, Subaru Hanada¹, Kazuaki Tanaka¹, Tomomi Takahashi¹ (1. Kyoto Institute of Technology)

キーワード：dialogue system, reinforcement learning, transfer learning, unsupervised learning

There is a growing need for dialogue systems that can interact appropriately with children. Pechat is a button-shaped speaker that is attached to a stuffed animal. Caregivers can have a voice dialogue with children through the stuffed animal by selecting speech on their smartphones. Our goal is to automate speech selection so that Pechat can interact with children without parental intervention. The system learned to select an appropriate response from a given set of candidates. The information available for the learning process was the caregiver's utterance selection history and the evaluation of the system utterance, which was judged by the prosodic features of the child's response. Prosody was used as a reward for reinforcement learning and defined the states of reinforcement learning. The experimental results revealed the difference in learning with and without the use of prosody.