How to Transcribe an Interview for Editing
To transcribe an interview for editing, get a transcript that carries timecode on every word, identifies the speakers, and is accurate enough to trust, then use it as the surface where you actually cut. A transcript made for reading and a transcript made for editing are different documents. The difference is timecode. Without it you have notes; with it you have an edit.
This is the step most people get half-right. They transcribe to read along, or to make captions, and never connect the text to the footage. But a transcript where each word is anchored to a frame turns highlighting a sentence into marking an in and out point. That is the whole foundation of transcript-based editing, the approach Adobe formalized when it shipped Text-Based Editing in Premiere Pro at NAB 2023.
What a good editing transcript needs
Word-level timecode. Non-negotiable for editing. Sentence-level or paragraph timestamps are fine for reading but useless for precise cuts. You want every word tied to a frame so a select is a clip.
Speaker labels. In any interview with more than one voice, diarization, the automatic splitting of who said what, saves enormous time. A wall of unattributed text is hard to navigate; a transcript that knows the interviewer from the subject reads like a script.
Real accuracy. Modern AI transcription is good but not perfect. 2025 benchmarks put leading speech-to-text in the range of 5 to 15 percent word error rate on clean conversational audio, with the best engines under 5 percent in ideal conditions. That degrades fast with background noise, crosstalk, or strong accents, so plan for a quick correction pass on anything but pristine audio.
AI versus human transcription
AI transcription is cheap and near-instant, and for clean single-speaker audio it is usually accurate enough to edit from with light cleanup. Services like Rev, Otter, and Descript reach the high 90s on good recordings. Human transcription costs more and takes longer but handles messy audio, heavy accents, and technical jargon far better, which is why legal and medical work still leans on it.
For most video editing, AI is the right default. You get the transcript in minutes, fix the handful of errors you spot, and you are cutting. The exception is footage where accuracy is mission-critical or the audio is genuinely rough, where a human pass earns its cost.
From transcript to edit
The transcript is not the destination, it is the start. Once you have a timecoded, speaker-labeled transcript, read it, highlight your selects, trim the fillers, and arrange the story, all on the page. In ScriptCut you work directly on the transcript: highlight a line and it becomes a precise cut because the word-level timecode is attached, then export the arrangement as a timeline to DaVinci Resolve, Premiere Pro, Final Cut, or Avid. The reason this is worth it is speed, reading runs about 238 words a minute versus roughly 150 for speech, per Brysbaert's 2019 meta-analysis, so the transcript is the fastest route through the footage you will find.
A worked example
You record a two-person interview, 40 minutes, decent lavs in a quiet room. You run it through an AI transcription with diarization. Two minutes later you have a speaker-labeled transcript at maybe 95 percent accuracy. You skim it once, fixing a dozen misheard names and a couple of jargon terms, five minutes of cleanup. Now the transcript is trustworthy. You highlight selects, the text is timecode-locked so each is already a clip, arrange them, and export to your editor. Total time from raw audio to a rough cut: under an hour, most of it the read, not the typing.
Common mistakes
Transcribing without timecode. A transcript you cannot click back to the footage is a reading document, not an editing one. Insist on word-level timecode if you intend to cut from it.
Trusting AI accuracy blindly. Even 95 percent means one wrong word in twenty. On names, numbers, and technical terms, those errors matter. Always do a correction pass before you rely on the text.
Ignoring audio quality at the shoot. Transcription accuracy is mostly decided before you ever hit transcribe. Good mics, low noise, and minimal crosstalk do more for your transcript than any software setting.
Treating the transcript as the finish line. The point is not to have a transcript; it is to edit from it. If you transcribe and then go cut from scratch on the timeline, you have thrown away the advantage.
The honest tradeoffs
AI transcription trades a little accuracy for a lot of speed and cost savings, and on clean audio that trade is easy. On bad audio it is not: the time you spend correcting a 70-percent transcript can exceed what a human transcriptionist would have charged. Know your audio before you choose.
There is also the Errol Morris caution worth holding onto. A transcript flattens performance; it tells you the words, not the delivery. So even a perfect transcript is a map, not the territory. Use it to navigate fast, but verify tone on the clip before you commit a line. The transcript gets you to the right neighborhood; your ears pick the house.
The takeaway
Transcribe for editing, not just for reading: insist on word-level timecode and speaker labels, expect to correct AI output on anything but clean audio, and treat the transcript as the surface you cut from, not a document you set aside. Get this step right and everything downstream, selecting, cleaning, arranging, gets faster. Next, put the transcript to work by editing the interview faster, finding your best soundbites, and organizing your footage.
Turn your transcript into an edit in ScriptCut.
Sources
Frequently asked questions
How do I transcribe an interview for video editing?
Use a transcription tool that produces word-level timecode and speaker labels, not just plain text. AI transcription gives you a draft in minutes; correct the errors you spot, then edit directly from the transcript so each highlighted line becomes a precise cut you can export to your timeline.
How accurate is AI transcription?
On clean conversational audio, leading tools land roughly in the 5 to 15 percent word error rate range, with the best under 5 percent in ideal conditions, meaning high-90s accuracy. Background noise, crosstalk, and strong accents lower that, so always do a correction pass on names, numbers, and jargon.
Do I need speaker labels in the transcript?
For any multi-voice interview, yes. Diarization, automatically splitting who said what, turns an unreadable wall of text into something that reads like a script and is far faster to navigate when you are pulling selects.
Why does word-level timecode matter?
Because it turns reading into editing. When every word is anchored to a frame, highlighting a sentence marks an in and out point, so your transcript selection becomes a real clip you can arrange and export. Without it, the transcript is only good for reading or captions.