Does AI Transcription Work With Accents and Dialects?

Speech models are trained on large sets of voices, and some accents and dialects are better represented than others. This means a tool can work very well for one speaker and struggle with another. The honest answer is that it depends on the tool, the accent and the recording, so test before you rely on it.

Why accuracy varies

A model learns patterns from the voices it was trained on. Accents that are common in the training data tend to transcribe well, and less common ones may produce more errors. Dialect words, slang and mixed languages add to the difficulty, as do fast speech and overlapping voices.

Test it on your own speech

1. Record a short sample of the speaker, around a minute, with the same microphone you will use.
2. Transcribe it with the tool you are considering.
3. Count the errors, and note which words cause them.
4. Decide whether the error rate is acceptable for your use.

Correct without losing the voice

When you correct a transcript, keep the speaker’s words where they are accurate, even if they are not standard. Changing a dialect word to a standard one can misrepresent the speaker. Fix clear errors, and ask the speaker if you are unsure what they meant.

Mixed languages

When people switch between languages, many tools struggle. If your interviews do this, check whether the tool supports multiple languages, and test a sample of mixed speech before you commit.

Limits

A tool that works for one speaker may not work for another, and models change over time. Test each new speaker or project, and check the transcript against the audio before publishing a quote. The accuracy post covers the checking step in more detail.

Frequently asked questions

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