How to Improve Transcription Accuracy Before You Upload
· 7 min read
It is tempting to treat transcription accuracy as a property of the model. In practice, the recording and the settings you choose determine more of the outcome than the model version does.
What actually causes transcription errors
Three causes dominate: overlapping speech, distance from the microphone, and vocabulary the model has not seen. The first two are recording problems; the third is a settings problem.
Fix the recording first
Nothing downstream recovers information the microphone did not capture.
Get closer
A phone held near the speaker outperforms a conference-room microphone across the table. Distance adds reverb, which is what confuses recognition.
Reduce steady noise
Air conditioning and fans are predictable and removable. A light noise-reduction pass helps more than any post-editing.
Avoid overlapping speech
When two people talk at once, one of them is lost. If you control the recording, ask people not to interrupt.
Use the settings that exist
Set the language explicitly when the recording is mixed or starts with silence. Enable speaker separation when more than one person speaks. Supply key terms for product names, acronyms, and technical vocabulary when the service supports it.
Review in the right order
Numbers, then names, then speaker attribution, then everything else. Reading linearly is slower and you will still miss the same figures.
Search, do not read
Use find for each capitalised term and each numeral rather than reading the whole transcript.
Fix once, globally
Replace a misspelled name everywhere at once instead of correcting each occurrence.
What not to do
Do not re-upload the same file hoping for a better result; identical input gives identical output. Do not edit the transcript for style before the facts are correct.
Conclusion
Record closer, set the language, enable speaker labels, and review by search. Those four habits produce more improvement than switching services.