A Survey on Evaluation Metrics for Backchannel Prediction Models

In this paper we give an overview of the evaluation metrics used to measure the performance of backchannel prediction models. Both objective and subjective evaluation metrics are discussed. The survey shows that almost every backchannel prediction model is evaluated with a different evaluation metric. This makes comparison between developed models unreliable, even beside the other variables in play, such as different corpora, language, conversational setting, amount of data and/or definition of the term backchannel

Mirroring to Build Trust in Digital Assistants

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end we conducted a user study where subjects interacted with a digital assistant that responded in a way that either matched their conversational style, or did not. Using self-reported personality attributes and subjects' feedback on the interactions, we built models that can reliably predict a user's preferred conversational style.Comment: Preprin

Speaker-adaptive multimodal prediction model for listener responses

The goal of this paper is to analyze and model the variability in speaking styles in dyadic interactions and build a predictive algorithm for listener responses that is able to adapt to these different styles. The end result of this research will be a virtual human able to automatically respond to a human speaker with proper listener responses (e.g., head nods). Our novel speaker-adaptive prediction model is created from a corpus of dyadic interactions where speaker variability is analyzed to identify a subset of prototypical speaker styles. During a live interaction our prediction model automatically identifies the closest prototypical speaker style and predicts listener responses based on this ``communicative style". Central to our approach is the idea of ``speaker profile" which uniquely identifies each speaker and enables the matching between prototypical speakers and new speakers. The paper shows the merits of our speaker-adaptive listener response prediction model by showing improvement over a state-of-the-art approach which does not adapt to the speaker. Besides the merits of speaker-adapta-tion, our experiments highlights the importance of using multimodal features when comparing speakers to select the closest prototypical speaker style