Speech is untidy. People talk over one another, trail off mid-sentence, and use names no dictionary expects. Turning that into text you can quote, search and share is a precision problem, and this hub is how the TARGA Voice Desk works through it.
The guides fall into two halves. One half explains the technology, starting with how AI speech-to-text works, from sound wave to punctuation. The other half is practical: how to record, review, correct and export without losing a day to it.
Where machines are strong, and where people still win
Most disagreements about transcription are really disagreements about the recording. Clean audio suits automation. Difficult audio needs judgement. Human versus AI transcription sets out the full case, and the table below is the short version.
| Automation handles this well | A human reviewer earns their cost here |
|---|---|
| Clear audio, one speaker at a time, steady pace | Crosstalk, room echo, phone lines and poor microphones |
| Long recordings that need a fast first draft | Legal, medical or research work quoted word for word |
| Everyday vocabulary and predictable phrasing | Unusual names, strong accents and specialist terms |
| Bulk work where a quick clean-up pass is fine | Final copy that will be published without further checks |
Accuracy claims deserve the same scepticism. Word error rate explained shows what a percentage does and does not tell you, while speech recognition collects the technical background behind those figures.
A workflow that holds up
Most of the quality is decided before anyone presses transcribe. These four habits do more for a transcript than any change of tool.
- Record with intent: position the microphone, name the speakers at the start and ask people not to overlap, as set out in our guide to transcribing an interview.
- Agree the rules first: decide whether you want every stumble or a clean read, because editing to a standard is far quicker than inventing one halfway through.
- Correct once, properly: fix names, numbers and technical terms in a single pass, then leave the text alone rather than drifting into rewriting.
- Export for the destination: subtitles have their own rules, which the SRT and VTT guide explains line by line.
Running this across a team, rather than a single desk, is its own discipline. The guides for that sit under transcription workflow.
Privacy is not a separate subject here; it is part of the quality. Privacy and security in AI transcription covers consent, storage and deletion, and the same thinking shapes how we built TRANSCRIPT.YOU.
Four questions to ask before you upload a recording
- Consent: does everyone speaking know the audio is being recorded and processed?
- Sensitivity: would any part of this recording cause harm if it were read by a stranger?
- Retention: how long is the audio kept after the transcript has been produced?
- Deletion: can you remove both the file and the transcript when the work is finished?
