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.

The exact timing of a conversational turn conveys important information to a listener. Most turns are initiated within 250ms after the previous turn. However, interlocutors take longer to initiate certain types of turns: those that either require more cognitive processing or are socially dispreferred. Many dispreferred turns are also cognitively demanding, so it is difficult to attribute specific conversational delays to social or cognitive mechanisms. In this paper, we evaluate the relative contribution of cognitive and social variables to the timing of utterances in conversation. We focus on a type of turn that is socially dispreferred, cognitively demanding, and generally delayed: other-initiations of repair (OIRs). OIRs occur when a listener notices and decides to signal a comprehension problem (e.g., "What?"). We analyzed the Floor Transfer Offsets of 456 OIRs, and found that interlocutors initiated OIRs later when trouble sources had weaker discourse context or were shorter, and when the OIR was more face-threatening. Our results suggest that both cognitive and social variables contribute to the timing of delayed utterances in conversation. We discuss how attention, prediction, planning, and social preference manifest in the timing of turns.