Determining most suitable listener backchannel type for speaker's utterance

A major hurdle in achieving a dialogue system that enables smooth dialogue is to determine how to generate an appropriate response to a user's utterance. Previous research has focused mainly on estimating whether to make an utterance backchannel in response to the user's utterance. We go one step further by examining, for the first time, the relationship between the type of utterance backchannel to be used and intent and type of the speaker's utterance, known as a dialogue act (DA). Specifically, we propose a new method for classifying utterance backchannels into nine types. We also created a corpus consisting of the DAs of speaker utterances and the backchannel types of listener utterances then used it to analyze the relationship between a speaker's and listener's utterances. Our findings clarify that the occurrence frequencies of a listener's backchannel types significantly depend on the DAs of the speaker's utterances. Since the goal of our research is to construct a dialogue system that generates a more natural backchannel, this classification method, which determines certain types of aids from the speaker's DA, will be beneficial to such a system.