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.

本文研究了在成本敏感的情况下，如何以固定的时间预算自动选择错误修正的位置和大小，以最大化修正的错误数量，并提出了一种动态更新框架来训练纠错成本模型，进而实现高度适应性。该方法在模拟和实际的用户研究中均获得了有效的效率改进。