@article{Umair-Mertens-etal-2022.103,
 abstract = {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.},
 author = {Muhammad Umair and Julia Beret Mertens and Saul Albert and J. P. De Ruiter},
 day = {29},
 doi = {10.5210/dad.2022.103},
 issue = {1},
 journal = {Dialogue \& Discourse},
 month = {04},
 pages = {63--95},
 title = {GailBot: An automatic transcription system for Conversation Analysis},
 url = {https://journals.uic.edu/ojs/index.php/dad/article/view/11392},
 volume = {13},
 year = {2022}
}
