Brandon C. Roy

Abstract

Managing a large-scale speech transcription task with a team of human transcribers requires effective quality control and workload distribution. As it becomes easier and cheaper to collect massive audio corpora the problem is magnified. Relying on expert review or transcribing all speech multiple times is impractical. Furthermore, speech that is difficult to transcribe may be better handled by a more experienced transcriber or skipped entirely. We present a fully automatic system to address these issues. First, we use the system to estimate transcription accuracy from a a single transcript and show that it correlates well with intertranscriber agreement. Second, we use the system to estimate the transcription “difficulty” of a speech segment and show that it is strongly correlated with transcriber effort. This system can help a transcription manager determine when speech segments may require review, track transcriber performance, and efficiently manage the transcription process.