Transcribing Against Time
This work addresses cost-sensitive transcription correction for users needing efficient manual error correction, representing an incremental improvement over existing methods.
The paper tackles the problem of manually correcting errors in automatic speech transcripts under time constraints by proposing a dynamic updating framework that trains cost models during transcription, eliminating the need for prior transcriber enrollment. In a realistic user study, this method achieved an average efficiency improvement of 15%, with up to 42% for participants who deviated most from the initial model.
We investigate the problem of manually correcting errors from an automatic speech transcript in a cost-sensitive fashion. This is done by specifying a fixed time budget, and then automatically choosing location and size of segments for correction such that the number of corrected errors is maximized. The core components, as suggested by previous research [1], are a utility model that estimates the number of errors in a particular segment, and a cost model that estimates annotation effort for the segment. In this work we propose a dynamic updating framework that allows for the training of cost models during the ongoing transcription process. This removes the need for transcriber enrollment prior to the actual transcription, and improves correction efficiency by allowing highly transcriber-adaptive cost modeling. We first confirm and analyze the improvements afforded by this method in a simulated study. We then conduct a realistic user study, observing efficiency improvements of 15% relative on average, and 42% for the participants who deviated most strongly from our initial, transcriber-agnostic cost model. Moreover, we find that our updating framework can capture dynamically changing factors, such as transcriber fatigue and topic familiarity, which we observe to have a large influence on the transcriber's working behavior.