Journal
PROCEEDINGS OF THE 2022 17TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI '22)
Volume -, Issue -, Pages 991-996Publisher
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
Categories
Funding
- European Research Council (ERC) under the European Union [677270]
- Leverhulme Trust [PLP-2018-152]
- European Union [814072]
- European Research Council (ERC) [677270] Funding Source: European Research Council (ERC)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available