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Title: | Communication skills training intervention based on automated recognition of nonverbal signals |
Authors: | Pereira, M Hone, K |
Keywords: | social signals;communication skills training;media interviews;off-the-shelf emotion recognition technology |
Issue Date: | 6-May-2021 |
Publisher: | Association for Computing Machinery |
Citation: | Pereira, M. and Hone, K. (2021) 'Communication Skills Training Intervention Based on Automated Recognition of Nonverbal Signals',CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan [virtual], 8–13 May. New York, NY, USA: Association for Computing Machinery, Article no. 742, pp. 1-14. doi: 10.1145/3411764.3445324. |
Abstract: | There have been promising studies that show a potential of providing social signal feedback to improve communication skills. However, these studies have primarily focused on unimodal methods of feedback. In addition to this, studies do not assess whether skills are maintained after a given time. With a sample size of 22 this paper investigates whether multimodal social signal feedback is an efective method of improving communication in the context of media interviews. A pre-post experimental evaluation of media skills training intervention is presented which compares standard feedback with augmented feedback based on automated recognition of multimodal social signals. Results revealed signifcantly diferent training efects between the two conditions. However, the initial experiment study failed to show signifcant diferences in human judgement of performance. A 6-month follow-up study revealed human judgement ratings were higher for the experiment group. This study suggests that augmented selective multimodal social signal feedback is an efective method for communication skills training. |
URI: | https://bura.brunel.ac.uk/handle/2438/22932 |
DOI: | https://doi.org/10.1145/3411764.3445324 |
ISBN: | 9781450380966 |
Other Identifiers: | 742 |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. CHI ’21, May 08–13, 2021, Yokohama, Japan. | 1.52 MB | Adobe PDF | View/Open |
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