Praising behavior is an important method of communication. An existing study constructed models to predict praising skill, which indicates the degree to which the praise is done well, by using only unimodal behavior such as speech audio or visual behavior of a praiser who gives praise in dyad interactions. To improve prediction performance, a model should be constructed that uses various additional information. In this study, we propose two approaches to predict praising skill highly accurately. The first uses trimodal (multimodal) behaviors extracted from visual, acoustic, and linguistic modalities. The second uses the behaviors of the receiver of praise since the reaction of the receiver should differ depending on how good the praise is. For this study, we collect trimodal features and the degree of praising skill in each praising scene in a dialogue. We construct multiple models to predict the degree of praising skills using various combinations of the trimodal features from the praiser and receiver. The experimental results show that the model that predicts praising skill most accurately uses multiple features related to both verbal and nonverbal behaviors of the praiser and receiver. Therefore, the two approaches of using trimodal behaviors and using features from both the receiver and praiser are effective for predicting praising skills in dyad interactions.

During dialogues, speakers need to be able to predict whether their partners understand their message. This is important for not only for human-to-human interaction but also human-to-agent interaction. We consider that if the listener's comprehension level can be automatically predicted, interactive agents will be able to communicate appropriately according to the user's comprehension level. However, to the best of our knowledge, there is no case study that reveals how comprehension can be predicted based on multimodal information about the listener. In this study, we attempt to predict comprehension levels on the basis of the listener's multimodal information. First, we construct a dialogue corpus consisting of the listener's comprehension levels and the listener's multimodal information. Next, we construct machine learning models that predict the listener's comprehension levels on the basis of the listener's multimodal information. Our results suggest that our model was able to predict a listener's comprehension level on the basis of a listener's multimodal information. In addition, two movements, the lifting of the cheeks and the pulling up of the corners of the lips, were suggested to be important in assessing the listener's level of comprehension.

The listener's backchannels are an important part of dialogues. With appropriate backchannels, people are able to smoothly promote dialogues. Thus, backchannels are considered to be important in dialogues between not only humans but also humans and agents. Progress has been made in studying dialogue agents that perform natural affable dialogue. However, we have not clarified whether the listener's various backchannel types are predictable using the speaker's multimodal information. In this paper, we attempt to predict a listener's various backchannel types on the basis of the speaker's multimodal information in dialogues. First, we construct a dialogue corpus that consists of multimodal information of a speaker's utterances and a listener's backchannels. Second, we construct machine learning models to predict a listener's various backchannel types on the basis of a speaker's multimodal information. Our results suggest that our model was able to predict a listener's various backchannel types on the basis of a speaker's multimodal information.

Praising behavior is considered to an important method of communication in daily life and social activities. An engineering analysis of praising behavior is therefore valuable. However, a dialogue corpus for this analysis has not yet been developed. Therefore, we develop corpuses for face-to-face and remote two-party dialogues with ratings of praising skills. The corpuses enable us to clarify how to use verbal and nonverbal behaviors for successfully praise. In this paper, we analyze the differences between the face-to-face and remote corpuses, in particular the expressions in adjudged praising scenes in both corpuses, and also evaluated praising skills. We also compare differences in head motion, gaze behavior, facial expression in high-rated praising scenes in both corpuses. The results showed that the distribution of praising scores was similar in face-to-face and remote dialogues, although the ratio of the number of praising scenes to the number of utterances was different. In addition, we confirmed differences in praising behavior in face-to-face and remote dialogues.

In this work, as a first attempt to analyze the relationship between praising skills and human behavior in dialogue, we focus on head and face behavior. We create a new dialogue corpus including face and head behavior information of persons who give praise (praiser) and receive praise (receiver) and the degree of success of praising (praising score). We also create a machine learning model that uses features related to head and face behavior to estimate praising score, clarify which features of the praiser and receiver are important in estimating praising score. The analysis results showed that features of the praiser and receiver are important in estimating praising score and that features related to utterance, head, gaze, and chin were important. The analysis of the features of high importance revealed that the praiser and receiver should face each other without turning their heads to the left or right, and the longer the praiser's utterance, the more successful the praising.