How can I stop dictation getting names and technical words wrong?

Quick answer — November 1, 2026: Use a dictation app with personal text replacements. Dictop can replace recurring misrecognitions before insertion, including names, product terms and abbreviations. It also supports regex rules and initial context for supported Whisper models. Replacements transform recognized text; they do not train the speech model or guarantee recognition of unfamiliar words.

A recognizer may understand most of a paragraph and still get your company's name wrong every time. Those errors are frustrating because they repeat. Fixing the same spelling manually after every recording wastes the benefit of dictating.

Start by collecting the actual mistakes, then choose the narrowest correction that solves them.

Turn a repeated error into a rule

Suppose a model repeatedly transcribes your fictional product name “LumaDesk” as “Luma desk.” Create a replacement from that observed phrase to the desired spelling. Dictate several sentences and check that the output changes only where intended.

In Dictop, replacement rules run after speech recognition and before optional AI refinement. You can deliver the replaced text directly, without asking an AI editor to infer the correct brand spelling each time.

Your model may recognize the name differently. Use the wording from your own recordings when you create a rule.

When should you use regex?

Exact replacements are a good first choice for a stable phrase. Regular expressions are useful when you need a pattern, such as several variants of the same recurring text or a structured formatting transformation. Dictop supports capture groups and empty replacements for deletion.

Keep patterns narrow and test them on both matching and nonmatching sentences. A rule that removes a filler word everywhere can also delete a word you intended to keep. Broad replacements are especially risky across languages or when a term is also an ordinary word.

Dictop provides a searchable rule list, JSON editing and predefined sets. As your vocabulary grows, use those controls to manage your rules. Add imported rules gradually so an unwanted change is easier to trace.

Can context help the recognizer itself?

The Transcription Context setting can guide supported Whisper models. A short list of relevant terms can help recognition, though it does not guarantee the desired spelling. Keep it relevant to the task and compare recordings with and without it.

For persistent mistakes, also try another compatible speech model. Vocabulary context, text replacement and model choice address different parts of the process.

Finally, use on-demand recording with history enabled to compare the original and replaced versions. Continuous mode saves the processed text without those separate versions. If optional AI refinement changes a corrected name again, tighten the editing prompt and review the final version.

Try Dictop for Mac. See the complete Mac guide for replacement rules and Whisper context settings.