What is the difference between verbatim and clean verbatim transcription?

Verbatim captures every utterance including filler words and false starts. Clean verbatim removes verbal clutter while preserving every factual statement and the speaker's meaning. Most professional use cases — journalism, research notes, corporate documentation — use clean verbatim.

How accurate is AI transcription for interviews with technical jargon?

On clear audio with a single speaker, modern AI transcription is typically above 95% accurate for standard vocabulary. Accuracy drops significantly on domain-specific jargon, unusual names, and acronyms — which is exactly where interviews tend to be densest. Using a custom Vocabulary Library before transcribing addresses this directly.

Do I need to transcribe the whole interview or just the relevant parts?

You only need to transcribe what you intend to use. For qualitative research, transcribing the full interview gives you the context to understand individual statements. For journalism or corporate documentation, transcribing only the sections you plan to quote or reference is usually sufficient.

Can I use AI-generated interview transcripts as legal evidence?

Consult a lawyer. Generally, AI transcripts are not directly admissible as legal evidence without verification. For anything that may enter legal proceedings, use a certified human transcription service.

How do I handle an interview participant who asked not to be recorded?

Take handwritten or typed notes in real time. Do not record without consent. If consent is withdrawn mid-interview, stop the recording immediately.

Interview Transcription Best Practices: Accurate, Usable Notes Every Time

I have been on both sides of interview transcription. As a business analyst, I was the one taking notes while simultaneously trying to ask the right follow-up questions — which is the transcription equivalent of rubbing your stomach and patting your head. As someone who has since built a transcription tool, I have also looked at what actually goes wrong when teams try to document interviews at scale.

The short version: most interview transcription problems start before anyone hits record.

TL;DR: Record in the best conditions you can. Choose your transcription style before you start. Use AI for the first draft. Review names, numbers, and quotes you plan to publish. Protect participant privacy by default.

Step 1: Get a better recording

Everything else in this guide is easier if the recording is clean. A few things that make a measurable difference:

Use separate microphones. If the interview is remote (Zoom, Teams, Google Meet), both participants should be on headsets or USB microphones — not laptop built-ins from across a room.
Record in a quiet space. Background noise is the number one accuracy killer. Close the door, turn off fans, and — if you are recording in a coffee shop — reconsider the coffee shop.
Ask participants to say their name at the start. "Hi, I'm Dr. Sarah Chen from..." at the beginning of the recording gives the AI a clean example of how each voice sounds before the conversation gets dense.
For remote interviews, download the highest-quality file your platform offers. Zoom offers separate audio tracks; use them if available.

Step 2: Choose the right transcription style first

Before uploading anything, decide what format your readers need.

Full verbatim: captures every utterance — filler words ("um," "uh," "like"), false starts, stutters, and non-verbal sounds ([laughter], [sighs]).

Use for: legal proceedings, psychological analysis, linguistic research, and any situation where verbal delivery is the evidence.

Clean verbatim (recommended for most work): removes verbal clutter while preserving 100% of the meaning, facts, and tone.

Use for: journalism (adhering to standards from institutes like Poynter), qualitative research, corporate notes, recruiting, podcast show notes, and any document you plan to publish or share.

Choosing the wrong style upfront is the most common source of unnecessary rework. Decide before you transcribe, not after.

Step 3: Run the audio through an AI transcription tool

Upload to an AI tool with speaker detection enabled. With AudioMaktube, the transcript includes:

Automatic speaker separation with timestamps (free plan)
Vocabulary Library — add participant names, job titles, and any domain-specific terms so the AI gets them right on the first pass (Pro, $10/mo)
Ask Your Audio — find a specific quote or answer without reading the whole transcript (Pro, $10/mo)
Export in TXT, SRT, or PDF (various plans)

The Vocabulary Library is the feature that saves the most time in practice. Add every name and unusual term before transcribing. An AI that has never encountered "Synexia" or "AWU state machine" will invent something plausible and repeat it forty times. Fixing it afterward takes longer than setting up the library took.

Step 4: Review the important details — not everything

Do not proofread the entire transcript. Focus your human review where errors are most costly:

Participant names and titles — check every instance
Figures, statistics, and dates — verify against your notes
Quotations you plan to publish — listen to the clip again before attributing anything
Technical terms and product names — especially anything domain-specific that the AI might have guessed at
Inaudible passages — mark these as [inaudible 14:22] rather than guessing. A wrong guess attributed to a named person is a credibility problem.

For everything else, the AI draft is accurate enough to use as a working document.

Step 5: Protect participants from the start

Interview transcripts are sensitive records — often more sensitive than the interview felt at the time.

Confirm consent before recording. Many jurisdictions require all-party consent for a recorded conversation. University guidelines (like Yale's qualitative research standards) heavily emphasize not skipping this step.
Limit access to people who actually need it. A transcript shared too broadly is difficult to un-share.
Follow your organisation's retention policy. If you do not have one for interview data, write one before the next round of interviews.
Anonymise where required. If participants were promised anonymity, apply it consistently in the transcript — not just in the published version.

When you don't need AI transcription for an interview

Interviews under 10 minutes with simple questions and clear audio: manual transcription is faster than the upload-and-review cycle.
One-off quick reference notes: a brief typed summary of the key quotes is enough.
Any interview where nothing will be published or shared: your own notes, however imperfect, are sufficient.

AI transcription is for interviews that are complex enough, long enough, or sensitive enough that a human note-taker cannot reliably capture everything while also conducting the interview.