The taking of turns is a fundamental aspect of dialogue. Since it is difficult to speak and listen
at the same time, the participants need to coordinate who is currently speaking and when …

We introduce dGSLM, the first “textless” model able to generate audio samples of naturalistic
spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with …

Intelligent agents interacting with humans through conversation (such as a robot, embodied
conversational agent, or chatbot) need to receive feedback from the human to make sure …

Previous models of turn-taking have mostly been trained for specific turn-taking decisions,
such as discriminating between turn shifts and turn retention in pauses. In this paper, we …

Computational syntactic processing is a fundamental technique in natural language
processing. It normally serves as a pre-processing method to transform natural language …

The modeling of turn-taking in dialog can be viewed as the modeling of the dynamics of
voice activity of the interlocutors. We extend prior work and define the predictive task of …

Syntactic and pragmatic completeness is known to be important for turn-taking prediction,
but so far machine learning models of turn-taking have used such linguistic information in a …

A challenge of large-scale oral communication assessments is to feasibly assess a broad
construct that includes interactional competence. One possible approach in addressing this …

Most second language assessment researchers agree that interactional competence (IC) is
an important part of the construct of oral communication. However, measurement of IC has …

We propose in this paper a statistical model in the perspective of predicting listener's
feedbacks in a conversation. The first contribution of the paper is a study of the prediction of …