Designing An Automated Assessment of Public Speaking Skills Using Multimodal Cues

Lei Chen
Gary Feng
Chee Wee Leong
Jilliam Joe
Christopher Kitchen
Chong Min Lee


Traditional assessments of public speaking skills rely on human scoring. We report an initial study on the development of an automated scoring model for public speaking performances using multimodal technologies. Task design, rubric development, and human rating were conducted according to standards in educational assessment. An initial corpus of 17 speakers with 4 speaking tasks was collected using audio, video, and 3D motion capturing devices. A scoring model based on basic features in the speech content, speech delivery, and hand, body, and head movements significantly predicts human rating, suggesting the feasibility of using multimodal technologies in the assessment of public speaking skills. 

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