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GailBot: An automatic transcription system for Conversation Analysis

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journal contribution
posted on 03.05.2022, 13:25 by Muhammad Umair, Julia Mertens, Saul AlbertSaul Albert, Jan P. de Ruiter
Researchers studying human interaction, such as conversation analysts, psychologists, and linguists, all rely on detailed transcriptions of language use. Ideally, these should include so-called paralinguistic features of talk, such as overlaps, prosody, and intonation, as they convey important information. However, creating conversational transcripts that include these features by hand requires substantial amounts of time by trained transcribers. There are currently no Speech to Text (STT) systems that are able to integrate these features in the generated transcript. To reduce the resources needed to create detailed conversation transcripts that include representation of paralinguistic features, we developed a program called GailBot. GailBot combines STT services with plugins to automatically generate first drafts of transcripts that largely follow the transcription standards common in the field of Conversation Analysis. It also enables researchers to add new plugins to transcribe additional features, or to improve the plugins it currently uses. We describe GailBot’s architecture and its use of computational heuristics and machine learning. We also evaluate its output in relation to transcripts produced by both human transcribers and comparable automated transcription systems. We argue that despite its limitations, GailBot represents a substantial improvement over existing dialogue transcription software.

Funding

AFOSR grant FA9550-18-1-0465

School of Arts & Sciences and the School of Engineering at Tufts University

History

School

  • Social Sciences and Humanities

Department

  • Communication and Media

Published in

Dialogue & Discourse

Volume

13

Issue

1

Pages

63 - 95

Publisher

University of Illinois Libraries

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by University of Illinois Libraries under the Creative Commons Attribution 3.0 Unported Licence (CC BY 3.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/3.0/

Publication date

2022-04-29

Copyright date

2022

eISSN

2152-9620

Language

en

Depositor

Dr Saul Albert. Deposit date: 1 May 2022

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