In the field of the communication robots, many recent studies have focused on dialogue communication robots. This paper especially focused on the supporting robot for the conversation between humans. To help conversation between humans, we believe that the robots should have two abilities: estimate dialogue moods and behave suitably. In this paper, we propose dialogue moods estimation model. This paper, as the first step, focused on dialogues between two persons and construct estimation model for the dialogue moods observed by the third party. Because we believed that the dialogue moods are influenced by utterance time, which is extracted easily, the utterance intervals features are used to estimate the dialogue moods, for example, both solitary utterance intervals of leading speakers and following speakers, simultaneous utterance intervals, and silent intervals between two speakers. Using these utterance intervals features, we constructed the estimation model for dialogue moods by using Tree-Augmented Naive Bayes. Through the estimation experiment, we confirmed the availability of the estimation model for dialogue moods, in particular “excitement,” “seriousness,” and “closeness,” and the effective utterance intervals features for estimating dialogue moods. From the experimental results, it is suggested that the proposed model is effective for estimating dialogue moods.