document.pdf (463.9 kB)
GailBot: An automatic transcription system for Conversation Analysis
journal contribution
posted on 2022-05-03, 13:25 authored by Muhammad Umair, Julia Mertens, Saul AlbertSaul Albert, Jan P. de RuiterResearchers 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 & DiscourseVolume
13Issue
1Pages
63 - 95Publisher
University of Illinois LibrariesVersion
- VoR (Version of Record)
Rights holder
© The AuthorsPublisher 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
2022-04-29Copyright date
2022eISSN
2152-9620Publisher version
Language
- en