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the substantial increase in the number of online human-human conversationsand the usefulness of multimodal transcripts, there is a rising need forautomated multimodal transcription systems to help us better understand theconversations. In this paper, we evaluated three methods to perform multimodaltranscription. They were (1) Jefferson -- an existing manual system used widelyby the linguistics community, (2) MONAH -- a system that aimed to makemultimodal transcripts accessible and automated, (3) MONAH+ -- a system thatbuilds on MONAH that visualizes machine attention. Based on 104 participantsresponses, we found that (1) all text-based methods significantly reduced theamount of information for the human users, (2) MONAH was found to be moreusable than Jefferson, (3) Jefferson's relative strength was in chronemics(pace / delay) and paralinguistics (pitch / volume) annotations, whilst MONAH'srelative strength was in kinesics (body language) annotations, (4) enlargingwords' font-size based on machine attention was confusing human users asloudness. These results pose considerations for researchers designing amultimodal annotation system for the masses who would like a fully-automated orhuman-augmented conversational analysis system.

An empirical user-study of text-based nonverbal annotation systems for human-human conversations