In this work, we experiment with the use of smiling and laughter in order to help create more natural and efficient listening agents. We present preliminary results on a system which predicts smile and laughter sequences in one dialogue participant based on observations of the other participant's behavior. This system also predicts the level of intensity or arousal for these sequences. We also describe an audiovisual concatenative synthesis process used to generate laughter and smiling sequences, producing multilevel amusement expressions from a dataset of audiovisual laughs. We thus present two contributions: one in the generation of smiling and laughter responses, the other in the prediction of what laughter and smiles to use in response to an interlocutor's behaviour. Both the synthesis system and the prediction system have been evaluated via Mean Opinion Score tests and have proved to give satisfying and promising results which open the door to interesting perspectives.

This paper addresses the problem of evaluating engagement of the human participant by combining verbal and nonverbal behaviour along with contextual information. This study will be carried out through four different corpora. Four different systems designed to explore essential and complementary aspects of the JOKER system in terms of paralinguistic/linguistic inputs were used for the data collection. An annotation scheme dedicated to the labeling of verbal and non-verbal behavior have been designed. From our experiment, engagement in HRI should be multifaceted.

In this paper, we study the controllability of an Expressive TTS system trained on a dataset for a continuous control. The dataset is the Blizzard 2013 dataset based on audiobooks read by a female speaker containing a great variability in styles and expressiveness. Controllability is evaluated with both an objective and a subjective experiment. The objective assessment is based on a measure of correlation between acoustic features and the dimensions of the latent space representing expressiveness. The subjective assessment is based on a perceptual experiment in which users are shown an interface for Controllable Expressive TTS and asked to retrieve a synthetic utterance whose expressiveness subjectively corresponds to that a reference utterance.

In this paper, we present a dyadic conversation dataset involving topics related to moral emotions which are ethically relevant. To the best of our knowledge, it is the first dataset where the main focus is moral emotions. This dataset also focuses on speaker-listener reactions during a dyadic conversation. Although some of the currently available datasets contain dyadic conversations, they were not conceived with the idea of focusing on the speaker-listener setup. Thus making it difficult to use them to study reactions related to speakers and listeners. Some preliminary analyses of the data are presented as well as our thoughts on future work related to this dataset.

In this work, we established the foundations of a framework with the goal to build an end-to-end naturalistic expressive listening agent. The project was split into modules for recognition of the user's paralinguistic and nonverbal expressions, prediction of the agent's reactions, synthesis of the agent's expressions and data recordings of nonverbal conversation expressions. First, a multimodal multitask deep learning-based emotion classification system was built along with a rule-based visual expression detection system. Then several sequence prediction systems for nonverbal expressions were implemented and compared. Also, an audiovisual concatenation-based synthesis system was implemented. Finally, a naturalistic, dyadic emotional conversation database was collected. We report here the work made for each of these modules and our planned future improvements.