We developped a web app for ascribing verbal descriptions to expressive audiovisual utterances. These descriptions are limited to lists of adjectives that are either suggested via a navigation in emotional latent spaces built using discriminant analysis of BERT embeddings or entered freely by subjects. We show that such verbal descriptions collected on-line via Prolific on massive data (310 participants, 12620 labelled utterances up-to-now) provide Expressive Multimodal Text-to-Speech Synthesis with precise verbal control over desired emotional content

Verbal and nonverbal communication skills are essential for human-robot interaction, in particular when the agents are involved in a shared task. We address the specific situation when the robot is the only agent knowing about the plan and the goal of the task and has to instruct the human partner. The case study is a brick assembly. We here describe a multi-layered verbal depictor whose semantic, syntactic and lexical settings have been collected and evaluated via crowdsourcing. One crowdsourced experiment involves a robot instructed pick-and-place task. We show that implicitly referring to achieved subgoals (stairs, pillows, etc) increases performance of human partners.

We present a series of experiments investigating face-to-face interaction between an Embodied Conversational Agent (ECA) and a human interlocutor. The ECA is embodied by a video realistic talking head with independent head and eye movements. For a beneficial application in face-to-face interaction, the ECA should be able to derive meaning from communicational gestures of a human interlocutor, and likewise to reproduce such gestures. Conveying its capability to interpret human behaviour, the system encourages the interlocutor to show appropriate natural activity. Therefore it is important that the ECA knows how to display what would correspond to mental states in humans. This allows to interpret the machine processes of the system in terms of human expressiveness and to assign them a corresponding meaning. Thus the system may maintain an interaction based on human patterns. During a first experiment we investigated the ability of our talking head to direct user attention with facial deictic cues (Raidt, Bailly et al. 2005). Users interact with the ECA during a simple card game offering different levels of help and guidance through facial deictic cues. We analyzed the users’ performance and their perception of the quality of assistance given by the ECA. The experiment showed that users profit from its presence and its facial deictic cues. In the continuative series of experiments presented here, we investigated the effect of an enhancement of the multimodality of the deictic gestures by adding a spoken instruction.