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Humans convey their intentions through the usage of both verbal and nonverbal behaviors during face-to-face communication. Speaker intentions often vary dynamically depending on different nonverbal contexts, such as vocal patterns and facial expressions. As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear. To better model human language, we first model expressive nonverbal representations by analyzing the fine-grained visual and acoustic patterns that occur during word segments. In addition, we seek to capture the dynamic nature of nonverbal intents by shifting word representations based on the accompanying nonverbal behaviors. To this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN) that models the fine-grained structure of nonverbal subword sequences and dynamically shifts word representations based on nonverbal cues. Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition. We also visualize the shifted word representations in different nonverbal contexts and summarize common patterns regarding multimodal variations of word representations.
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Investigating Therapist Vocal Nonverbal Behavior for Applications in Robot-Mediated Therapies for Individuals Diagnosed with Autism
Socially assistive robots (SARs) are being utilized for delivering a variety of healthcare services to patients. The design of these human-robot interactions (HRIs) for healthcare applications have primarily focused on the interaction flow and verbal behaviors of a SAR. To date, there has been minimal focus on investigating how SAR nonverbal behaviors should be designed according to the context of the SAR’s communication goals during a HRI. In this paper, we present a methodology to investigate nonverbal behavior during specific human-human healthcare interactions so that they can be applied to a SAR. We apply this methodology to study the context-dependent vocal nonverbal behaviors of therapists during discrete trial training (DTT) therapies delivered to children with autism. We chose DTT because it is a therapy commonly being delivered by SARs and modeled after human-human interactions. Results from our study led to the following recommendations for the design of the vocal nonverbal behavior of SARs during a DTT therapy: 1) the consequential error correction should have a lower pitch and intensity than the discriminative stimulus but maintain a similar speaking rate; and 2) the consequential reinforcement should have a higher pitch and intensity than the discriminative stimulus but a slower speaking rate.
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- Award ID(s):
- 1948224
- PAR ID:
- 10221653
- Date Published:
- Journal Name:
- International Conference on Social Robotics
- Page Range / eLocation ID:
- 416-427
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-62056-1_35
