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

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

Continuous Interaction with a Virtual Human

Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access

Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor

Using supporting backchannel (BC) cues can make human-computer interaction more social. BCs provide a feedback from the listener to the speaker indicating to the speaker that he is still listened to. BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues. In this work, we only considered acoustic cues. We are proposing an approach towards detecting BC opportunities based on acoustic input features like power and pitch. While other works in the field rely on the use of a hand-written rule set or specialized features, we made use of artificial neural networks. They are capable of deriving higher order features from input features themselves. In our setup, we first used a fully connected feed-forward network to establish an updated baseline in comparison to our previously proposed setup. We also extended this setup by the use of Long Short-Term Memory (LSTM) networks which have shown to outperform feed-forward based setups on various tasks. Our best system achieved an F1-Score of 0.37 using power and pitch features. Adding linguistic information using word2vec, the score increased to 0.39

Multimodal Dyadic Impression Recognition via Listener Adaptive Cross-Domain Fusion

As a sub-branch of affective computing, impression recognition, e.g., perception of speaker characteristics such as warmth or competence, is potentially a critical part of both human-human conversations and spoken dialogue systems. Most research has studied impressions only from the behaviors expressed by the speaker or the response from the listener, yet ignored their latent connection. In this paper, we perform impression recognition using a proposed listener adaptive cross-domain architecture, which consists of a listener adaptation function to model the causality between speaker and listener behaviors and a cross-domain fusion function to strengthen their connection. The experimental evaluation on the dyadic IMPRESSION dataset verified the efficacy of our method, producing concordance correlation coefficients of 78.8% and 77.5% in the competence and warmth dimensions, outperforming previous studies. The proposed method is expected to be generalized to similar dyadic interaction scenarios.Comment: Accepted to ICASSP2023. arXiv admin note: substantial text overlap with arXiv:2203.1393

Learning and Evaluating Response Prediction Models using Parallel Listener Consensus

Traditionally listener response prediction models are learned from pre-recorded dyadic interactions. Because of individual differences in behavior, these recordings do not capture the complete ground truth. Where the recorded listener did not respond to an opportunity provided by the speaker, another listener would have responded or vice versa. In this paper, we introduce the concept of parallel listener consensus where the listener responses from multiple parallel interactions are combined to better capture differences and similarities between individuals. We show how parallel listener consensus can be used for both learning and evaluating probabilistic prediction models of listener responses. To improve the learning performance, the parallel consensus helps identifying better negative samples and reduces outliers in the positive samples. We propose a new error measurement called Fconsensus which exploits the parallel consensus to better define the concepts of exactness (mislabels) and completeness (missed labels) for prediction models. We present a series of experiments using the MultiLis Corpus where three listeners were tricked into believing that they had a one-on-one conversation with a speaker, while in fact they were recorded in parallel in interaction with the same speaker. In this paper we show that using parallel listener consensus can improve learning performance and represent better evaluation criteria for predictive models

Observations on listener responses from multiple perspectives

Proceedings of the 3rd Nordic Symposium on Multimodal Communication. Editors: Patrizia Paggio, Elisabeth Ahlsén, Jens Allwood, Kristiina Jokinen, Costanza Navarretta. NEALT Proceedings Series, Vol. 15 (2011), 48–55. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/22532

Iterative Perceptual Learning for Social Behavior Synthesis

We introduce Iterative Perceptual Learning (IPL), a novel approach for learning computational models for social behavior synthesis from corpora of human-human interactions. The IPL approach combines perceptual evaluation with iterative model refinement. Human observers rate the appropriateness of synthesized individual behaviors in the context of a conversation. These ratings are in turn used to refine the machine learning models. As the ratings correspond to those moments in the conversation where the production of a specific social behavior is inappropriate, we can regard features extracted at these moments as negative samples for the training of a machine learning classifier. This is an advantage over traditional corpusbased approaches, in which negative samples at extracted at random from moments in the conversation where the specific social behavior does not occur. We perform a comparison between the IPL approach and the traditional corpus-based approach on the timing of backchannels for a listener in speaker-listener dialogs. While both models perform similarly in terms of precision and recall scores, the results of the IPL model are rated as more appropriate in the perceptual evaluation.We additionally investigate the effect of the amount of available training data and the variation of training data on the outcome of the models

Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness