3.8 Proceedings Paper

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

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1109/HRI53351.2022.9889431

Keywords

Datasets; Neural Networks; Speech Recognition; Human-robot Interaction; Behavioral Health; Non-intrusive sensing technology; Communication; Perception; Affective computing

Funding

  1. European Research Council (ERC) under the European Union [677270]
  2. Leverhulme Trust [PLP-2018-152]
  3. European Union [814072]
  4. European Research Council (ERC) [677270] Funding Source: European Research Council (ERC)

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This paper focuses on how different deep learning architectures, trained on various interaction data, can help artificial agents extract meaningful information about people's subjective perceptions in speech-based interactions. The study prioritizes high quality data over complex model architectures and demonstrates that both standard and a novel neural network architecture can successfully extract features related to subjective self-disclosure from speech data.
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 self-disclosure.

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