Machine or Person? Comparing AI and Human Transcription

What decides accuracy for each

Accuracy is not a fixed property of either option. It depends heavily on the recording.

For AI transcription, the main drivers are audio quality, background noise and music, how far speakers are from the microphone, overlapping speech, accents and dialects that were less common in the model's training data, and specialist vocabulary such as drug names, legal terms or product codes. On a clean, single-speaker recording, a modern model like OpenAI's Whisper can produce a transcript that needs only light correction. On a noisy, crosstalk-heavy group discussion, it can miss or garble whole passages.

Human transcribers are affected by the same audio problems, but they bring things a model lacks: they can look up a name, recognize when a word does not make sense in context, mark a passage as inaudible rather than guessing, and ask a question. Their accuracy depends on skill, familiarity with the subject and the time they are given.

Turnaround

Machine transcription generally runs much faster than real time, so the text for a long recording arrives far sooner than a person could type it. A human transcriber has to listen, type and check, which takes considerably longer than the recording itself, and services add queue time on top. Rush delivery is usually available from human services at a higher price.

For one recording this may not matter. For a backlog of dozens of hours, or a workflow that needs text the same day, it decides the question.

Cost structure

The two are priced on the same unit, the audio minute, but at very different levels. AI transcription costs little enough that it is reasonable to transcribe everything and decide later what is worth reading. Human services usually charge more for difficult audio, many speakers, strict verbatim, specialist subjects and fast turnaround, so get a quote based on a representative sample. With mydubly, transcription is 1 credit per minute: an hour of audio is 60 credits, or 6 cents.

Confidentiality

The privacy question differs in kind. With a human service, a person listens to the whole recording, so you rely on their confidentiality agreement and the service's handling of files. With AI transcription, nobody needs to listen, but the audio is processed on a provider's servers, so you rely on its retention and access policies. Neither option is automatically compliant with any particular regulation or research ethics approval, and some recordings should not leave your control at all. The questions worth asking any provider are listed in cloud speech processing privacy.

Speaker labels, timestamps and formatting

Human services routinely label speakers, follow style guides, apply clean or strict verbatim on request and add timestamps at set intervals. AI tools vary. Many provide segment timestamps by default; speaker labeling, called diarization, is offered by some and not others, and it makes mistakes when voices are similar or overlap. mydubly's transcripts carry timestamps on every segment but do not label speakers.

Where AI transcription falls short

• Hard audio: noise, distance, echo and overlapping voices degrade machine output faster than human output.
• Silence and music: Whisper-style models can occasionally produce text that was never said in long silences or music; Whisper hallucinations explains why.
• Names and jargon: unfamiliar proper nouns and technical terms are often spelled phonetically or replaced with common words.
• Verbatim detail: models tend toward clean output and drop many fillers and false starts, which matters for linguistics or some legal work.
• No judgment calls: a model will not flag a passage as uncertain the way a careful transcriber marks something inaudible.

Where human transcription falls short

• Cost and time make it impractical to transcribe large archives or every meeting.
• Quality varies between transcribers, and long jobs may be split among several people with different habits.
• A person hears everything, which may not be acceptable for some sensitive material.
• Fixes and re-runs are slow and cost money each time.

Hybrid workflows that combine both

Many teams now use AI for the draft and people for the parts that matter. A typical workflow:

1. Transcribe everything with AI to get searchable, timestamped text quickly.
2. Use the timestamps to find the passages you will actually quote, publish or analyze.
3. Have a person, yourself, a colleague or a professional, review and correct only those passages against the audio.
4. Send recordings with very poor audio, heavy crosstalk or strict verbatim needs to a human service from the start.
5. Keep a list of names and terms that the AI got wrong, and check for them first in later transcripts.

AI transcription with mydubly

mydubly's transcription runs on Whisper, by default the large-v3-turbo model. Use audio to text for MP3, WAV, M4A, AAC, OGG or FLAC files, or video to text for MP4, MOV, WebM, MKV or M4V; files up to 2 hours are supported. You get a timestamped transcript and SRT and VTT files, with the spoken language detected automatically from 21 supported languages, and an optional translation.

There are no speaker labels, so plan to add them yourself for interviews and group discussions. Uploaded audio and results are deleted within 30 minutes of a job finishing. For reviewing what comes back, how to proofread an AI transcript gives a quick method.

Where to go next

To see how AI handles your own audio, transcribe a representative 5-minute clip on audio to text; it costs the 5-credit minimum. Read it against the recording, and you will know quickly whether AI alone, a hybrid or a human service fits the project. For interview-heavy work, interview transcription covers the specifics.