Towards a Multimodal Time-Based Empathy Prediction System

We describe our system for empathic emotion recognition. It is based on deep learning on multiple modalities in a late fusion architecture. We describe the modules of our system and discuss the evaluation results. Our code is also available for the research community

Translating novel findings of perceptual-motor codes into the neuro-rehabilitation of movement disorders

The bidirectional flow of perceptual and motor information has recently proven useful as rehabilitative tool for re-building motor memories. We analyzed how the visual-motor approach has been successfully applied in neurorehabilitation, leading to surprisingly rapid and effective improvements in action execution. We proposed that the contribution of multiple sensory channels during treatment enables individuals to predict and optimize motor behavior, having a greater effect than visual input alone. We explored how the state-of-the-art neuroscience techniques show direct evidence that employment of visual-motor approach leads to increased motor cortex excitability and synaptic and cortical map plasticity. This super-additive response to multimodal stimulation may maximize neural plasticity, potentiating the effect of conventional treatment, and will be a valuable approach when it comes to advances in innovative methodologies

Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction

Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users' impressions of the robot after a conversation. We find that happiness in the user's recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that facial expression, emotional features, and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning based adaptive human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table

Spring School on Language, Music, and Cognition: Organizing Events in Time

The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session. Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations

Performance analysis of unimodal and multimodal models in valence-based empathy recognition

The human ability to empathise is a core aspect of successful interpersonal relationships. In this regard, human-robot interaction can be improved through the automatic perception of empathy, among other human attributes, allowing robots to affectively adapt their actions to interactants' feelings in any given situation. This paper presents our contribution to the generalised track of the One-Minute Gradual (OMG) Empathy Prediction Challenge by describing our approach to predict a listener's valence during semi-scripted actor-listener interactions. We extract visual and acoustic features from the interactions and feed them into a bidirectional long short-term memory network to capture the time-dependencies of the valence-based empathy during the interactions. Generalised and personalised unimodal and multimodal valence-based empathy models are then trained to assess the impact of each modality on the system performance. Furthermore, we analyse if intra-subject dependencies on empathy perception affect the system performance. We assess the models by computing the concordance correlation coefficient (CCC) between the predicted and self-annotated valence scores. The results support the suitability of employing multimodal data to recognise participants' valence-based empathy during the interactions, and highlight the subject-dependency of empathy. In particular, we obtained our best result with a personalised multimodal model, which achieved a CCC of 0.11 on the test set.Funding : Bavarian State Ministry of Education, Science and the Arts Electronic ISBN:978-1-7281-0089-0 IEEE restrictions of institutional members and purchase

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior