Dialects and Speech AI: Why Regional Speech Challenges Transcription — memozero

How well does an AI actually understand dialect? We hear this question regularly, especially from forensic experts, law enforcement agencies, and researchers who work with recordings from many different regions. The honest answer: dialects are always a challenge in computational linguistics, and for several reasons at once. In this article, we explain why that is, what it actually means for your work, and how memozero supports you.

Dialects are more than “accented standard speech”

Some language varieties effectively constitute languages of their own. Scots, for example, spoken in Scotland, is widely regarded by linguists as a distinct language rather than a dialect of English. African American Vernacular English (AAVE) likewise differs from Standard American English not only in pronunciation but also in vocabulary and grammar, and in some cases even in spelling, insofar as an established spelling exists at all.

These varieties can in turn be split into further sub-varieties with their own pronunciation, spelling habits, and vocabulary. One example: within the American South, Southern American English and Appalachian English differ considerably, even though the regions lie close together. The same holds in the Northeast, where the Boston accent and the New York accent are clearly distinct despite the short geographic distance between the two cities.

Different historical roots play a role as well. Cajun English in Louisiana grew out of contact with Louisiana French, while Appalachian English preserves features of older Scots-Irish settlement speech. And varieties keep mixing: through migration and media, blended regional forms emerge, a process linguists call dialect levelling.

What would an AI have to accomplish?

For an AI to capture all these varieties under “English”, reliably recognize their distinctive words and pronunciation, and transcribe them with sufficient precision, nuance, and without recurring errors is, to our knowledge, not (yet) possible at present.

The reason lies in how modern speech recognition works: the models learn from enormous amounts of audio recordings paired with matching transcripts. Standard speech is massively overrepresented in this training data, which is why standard English works so well. Faithful recognition of a specific dialect, by contrast, would require tens of thousands of longer recordings of speakers of that dialect, each with already perfect transcripts, taught to an AI word by word over multiple training passes. To make matters worse, some dialects have no generally accepted spelling at all: what would the “correct” transcript even look like?

In practice this means: the closer a recording is to standard speech, the better the result. With a regional accent, recognition usually remains very good. With deep dialect, the model often falls back on the closest standard-language word, and it is precisely these passages that need correcting afterwards.

What does this mean for expert-witness practice?

The good news: in practice, especially in expert-witness contexts, a dialect-faithful transcription is usually not required in the first place. A verbatim transcription that faithfully captures the content of what was said is generally sufficient; in Germany, for example, the country’s Federal Court of Justice confirmed this in a landmark 1999 ruling on credibility assessment.

In our view, the reasoning centers on content analysis. For criteria-based content analysis, the identification of reality criteria in a statement, the exact regional pronunciation should rarely matter, because this analytical step depends on the content-related (not the paraverbal) aspects of a statement. What counts is what was said, not how it was pronounced regionally.

How memozero supports you in practice

Because dialect-related errors can never be fully avoided technically, we built memozero so that you spend as little time as possible on correction:

• Synchronized playback: Text and audio run in sync in the editor. You hear every unclear passage in the original and correct it directly in the text, without searching through the audio.
• Highlighting of uncertain passages: memozero shows you, per word, how confident the model was. Dialect-related uncertainties stand out immediately.
• Custom dictionary: Add regional words, technical terms, and proper names that recur in your recordings. Results improve over time.
• Automatic speaker separation: Speaker attribution works regardless of dialect, because it is based on voice characteristics, not on what was said.

By the way: recording quality also plays a major role. A closely placed microphone and little background noise help the model disproportionately with dialect recordings.

Our conclusion

An AI that transcribes every dialect flawlessly and faithfully to its regional form does not currently exist, and that will not change in the short term. For reliable working results, however, it is not needed either: a verbatim, content-complete transcription with an efficient correction workflow covers the demands of practice.

We therefore recommend trying our trial version with your own audio material, with no strings attached. If you are not satisfied, you can simply cancel within the first two weeks at no cost. In that case, please let us know what we could improve or add from your perspective.