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Found 22 papers, 2 papers with code
Annotating Interruption in Dyadic Human Interaction
Integrating the existing interruption and turn switch classification methods, we propose a new annotation schema to annotate different types of interruptions through timeliness, switch accomplishment and speech content level.
META4: Semantically-Aligned Generation of Metaphoric Gestures Using Self-Supervised Text and Speech Representation
Our approach is the first method for generating speech driven metaphoric gestures while leveraging the potential of Image Schemas.
Seeing and hearing what has not been said; A multimodal client behavior classifier in Motivational Interviewing with interpretable fusion
To evaluate the quality of an MI conversation, client utterances can be classified using the MISC code as either change talk, sustain talk, or follow/neutral talk.
TranSTYLer: Multimodal Behavioral Style Transfer for Facial and Body Gestures Generation
Behavior expressivity style is viewed here as the qualitative properties of behaviors.
ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding
In this study, we address the importance of modeling behavior style in virtual agents for personalized human-agent interaction.
AMII: Adaptive Multimodal Inter-personal and Intra-personal Model for Adapted Behavior Synthesis
We propose AMII, a novel approach to synthesize adaptive facial gestures for SIAs while interacting with Users and acting interchangeably as a speaker or as a listener.
Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding
The third goal is to allow zero shot style transfer of speakers unseen during training without retraining the model.
Representation Learning of Image Schema
Therefore, after obtaining the vector representation of the image schemas, we calculate the distances between those vectors.
Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation
We propose a semantically-aware speech driven model to generate expressive and natural upper-facial and head motion for Embodied Conversational Agents (ECA).
Sequence-to-Sequence Predictive Model: From Prosody To Communicative Gestures
Our objective is to predict the timing of gestures according to the acoustic.
The ISO Standard for Dialogue Act Annotation, Second Edition
ISO standard 24617-2 for dialogue act annotation, established in 2012, has in the past few years been used both in corpus annotation and in the design of components for spoken and multimodal dialogue systems.
Multimodal Analysis of Cohesion in Multi-party Interactions
We also observe that, dialogue acts and head nods did not have an impact on the level of cohesion by itself.
Is Two Better than One? Effects of Multiple Agents on User Persuasion
In this paper, we present an evaluation study focused on understanding the effects of multiple agents on user's persuasion.
Emergence of synchrony in an Adaptive Interaction Model
In a Human-Computer Interaction context, we aim to elaborate an adaptive and generic interaction model in two different use cases: Embodied Conversational Agents and Creative Musical Agents for musical improvisation.
A model to generate adaptive multimodal job interviews with a virtual recruiter
This paper presents an adaptive model of multimodal social behavior for embodied conversational agents.
Mining a multimodal corpus for non-verbal behavior sequences conveying attitudes
Interpersonal attitudes are expressed by non-verbal behaviors on a variety of different modalities.
Emilya: Emotional body expression in daily actions database
In this paper, we describe our new database of emotional body expression in daily actions, where 11 actors express 8 emotions in 7 actions.
Expressing social attitudes in virtual agents for social training games
In this paper, we propose a model of social attitudes that enables a virtual agent to reason on the appropriate social attitude to express during the interaction with a user given the course of the interaction, but also the emotions, mood and personality of the agent.
