Automated Transcription and Conversation Analysis

Abstract

This article explores the potential of automated transcription technology for use in Conversation Analysis (CA). First, it applies auto-transcription to a classic CA recording and compares the output with Gail Jefferson’s original transcript. Second, it applies auto-transcription to more recent recordings to demonstrate transcript quality under ideal conditions. And third, it examines the use of auto-transcripts for navigating big conversational data sets. The article concludes that although standard automated transcription technology lacks certain critical capabilities and exhibits varying levels of accuracy, it may still be useful for (a) providing first-pass transcripts, with silences, for further manual editing; and (b) scaling up data exploration and collection building by providing time-based indices requiring no manual effort to generate. Data are in American English.