Enhancing Speaking and Slide Design Skills with Deep Learning: An Online Presentation Assessment System

Presentation skills, which involve the effective use of verbal and nonverbacl cues, enable audiences to better understand the content being presented. We develope a deep learning-based online assessment system that can objectively evaluate speakers' oral presentations and slide design, providing comprehensive feedback to support their self-practice. For the speaking skill assessment, we construct a multimodal neural network, including LSTMs and attention networks, to analyze the linguistic and acoustic features of oral presentations. The proposed model can predict 14 distinct types of audience impressions with an average accuracy of 85.0%. For the slide design assessment, we propose a method that can analyze slide design based on their visual and structural features, independent of file formats. It can determine whether the slides meet 10 assessment criteria with an average accuracy of 81.7%.

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Publication Details

Published
2024-10-26
DOI
https://doi.org/10.1145/3664647.3684999
Primary Topic
Subtitles and Audiovisual Media
Type
article
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Enhancing Speaking and Slide Design Skills with Deep Learning: An Online Presentation Assessment System

Toshihiko Yamasaki, Junichiro Matsugami, Shengzhou Yi, Tetsuro Yamamoto
Subtitles and Audiovisual Media
article

Enhancing Speaking and Slide Design Skills with Deep Learning: An Online Presentation Assessment System

Toshihiko Yamasaki, Junichiro Matsugami, Shengzhou Yi, Tetsuro Yamamoto
article en

Abstract

Presentation skills, which involve the effective use of verbal and nonverbacl cues, enable audiences to better understand the content being presented. We develope a deep learning-based online assessment system that can objectively evaluate speakers' oral presentations and slide design, providing comprehensive feedback to support their self-practice. For the speaking skill assessment, we construct a multimodal neural network, including LSTMs and attention networks, to analyze the linguistic and acoustic features of oral presentations. The proposed model can predict 14 distinct types of audience impressions with an average accuracy of 85.0%. For the slide design assessment, we propose a method that can analyze slide design based on their visual and structural features, independent of file formats. It can determine whether the slides meet 10 assessment criteria with an average accuracy of 81.7%.

The University of Tokyo (JP)
Quality Education
Openalex Percentile: Top 68%
Subtitles and Audiovisual Media
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Enhancing Speaking and Slide Design Skills with Deep Learning: An Online Presentation Assessment System — Toshihiko Yamasaki, Junichiro Matsugami, et al. · (2024) | TGRS Research Map | TGRS