Inside dictation cleanup: How raw speech becomes finished text

Dictation systems typically involve two distinct stages: speech recognition, which produces a verbatim transcript, and a subsequent rewrite pass that removes disfluencies such as filler words, false starts, repeats, and some corrections to create readable text. Because rewriting can only polish the words recognized in the first stage, it cannot repair misheard names, technical terms, medications, or other entities; those errors must instead be addressed upstream through domain context, keyterms, conversation history, or specialized recognition modes. The discussion emphasizes conservative cleanup instructions, since overly aggressive rewriting can remove meaningful content or create polished but inaccurate output, and recommends planning for rewrite failures by retaining usable raw transcripts. Verbatim transcription remains preferable for legal, compliance, and certain medical workflows where exact wording, spoken punctuation, or hesitation patterns may matter. It also notes tradeoffs such as custom contextual prompts potentially preventing explicit language selection, and argues that evaluation should prioritize entity accuracy and clearly specified rules about what cleanup must never alter rather than broad word-error metrics alone.

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