Is Deep Learning a Valid Approach for Inferring Subjective Self-Disclosure in Human-Robot Interactions?

Abstract

One limitation of social robots has been the ability of the models they operate on to infer meaningful social information about people's subjective perceptions, specifically from non-invasive behavioral cues. Accordingly, our paper aims to demonstrate how different deep learning architectures trained on data from human-robot, human-human, and human-agent interactions can help artificial agents to extract meaning, in terms of people's subjective perceptions, in speech-based interactions.Here we focus on identifying people's perceptions of their subjective self-disclosure (i.e., to what extent one perceives to be sharing personal information with an agent). We approached this problem in a data-first manner, prioritizing high quality data over complex model architectures. In this context, we aimed to examine the extent to which relatively simple deep neural networks could extract non-lexical features related to this kind of subjective self perception. We show that five standard neural network architectures and one novel architecture, which we call a Hopfield Convolutional Neural Network, are all able to extract meaningful features from speech data relating to subjective selfdisclosure.

Building Long-Term Human–Robot Relationships: Examining Disclosure, Perception and Well-Being Across Time

“…Furthermore, this study can be replicated and tested with various populations, clinical and healthy, in order to understand how social robots could be introduced in different care settings and as interventions using speech-based interactions [c.f., 126]. By introducing this novel paradigm in detail here, and documenting results from a rigorous empirical study using this paradigm, we aim to provide a tool that we hope will be of use to the HRI research community more broadly, while also assisting with facilitating research rigour and reproducibility [1,8,127,128], as well as the development of data-centric robotic models [c.f., [129][130][131]. Moreover, we would argue that the online computer-mediated means of human-robot communication used in this experimental design can overcome some of the challenges and barriers that are related to long-term HRI studies in natural ecologically valid settings (such as the costs associated with sending individual robots home for an extended period of time with participants) and suggest alternative means for conducting HRI research in people's natural settings.…”