We have been working on listening-oriented dialogues for the purpose of building listening agents. In our previous work , we trained hidden Markov models (HMMs) from listening-oriented dialogues (LoDs) between humans, and by analyzing them, discovered a distinguishing dialogue flow of LoD. For example, listeners suppress their information giving and self-disclosure, and instead, increase acknowledgments and questions to elicit speakers' utterances. As an initial step for building listening agents, we decided to create dialogue control rules based on our analysis of the HMMs. We built our rule-based system and compared it with three other systems by a Wizard of Oz (WoZ) experiment. As a result, we found that our rule-based system achieved as much user satisfaction as human listeners.
Bibliographic reference. Meguro, Toyomi / Minami, Yasuhiro / Higashinaka, Ryuichiro / Dohsaka, Kohji (2011): "Evaluation of listening-oriented dialogue control rules based on the analysis of HMMs", In INTERSPEECH-2011, 809-812.