Dialects and speech AI: why vernacular challenges transcription
How well does an AI actually understand dialect? This question reaches us regularly, especially from expert witnesses, investigative authorities and researchers who work with recordings from the whole German-speaking area. The honest answer: dialects are always a challenge in computational linguistics, and that for several reasons. This article explains why that is so, what it actually means for your work and how memozero supports you.
Dialects are more than “tinted standard German”
Some dialects are in fact languages of their own, for example “Platt”, “Säggsisch” or “Schwyzerdütsch”. They differ from standard German not only in pronunciation, but also in vocabulary, grammar and sometimes even in spelling, if a recognised spelling exists at all.
These languages can in turn split into dialects of their own, with a specific spoken and written form and their own words. One example: in Swiss German there are large differences between Bernese German and Zurich German, even though the two regions lie close together.
The same pattern exists in Germany. Kölsch and Ruhr German differ considerably in places, because they grew out of Low German and Central German respectively. The “Fränkisch” and “Altbairisch” dialects likewise have different roots, and mixed forms have also appeared in the meantime, so-called regiolects. Such mixed forms can also be observed in the south-west of Baden-Württemberg, where Alemannic Badisch meets Swabian.
What dialects demand of an AI
That an AI captures all these dialects under “German”, reliably recognises their own words and pronunciation, and writes them down with enough distinction, but still with nuance and without recurring errors, is to our knowledge not possible at present.
The reason lies in how modern speech recognition works: the models learn from huge amounts of audio recordings with matching transcripts. Standard language is heavily over-represented in these training data, which is why standard German works very well. A transcription that stays true to the dialect would instead need tens of thousands of longer recordings from speakers of each dialect, with already perfect transcripts, which an AI would have to be taught word by word in several passes. A further difficulty is that some dialects have no generally recognised spelling: what would then even be the “correct” transcription?
In practice that means, concretely: the closer a recording is to the standard language, the better the result. With a regional colouring the recognition usually remains very good. With a deep dialect the model often falls back on the closest standard-German word, and those are the places you then need to correct.
What does that mean for expert practice?
The good news: for practice, especially in the context of an expert opinion, a dialect-true transcription is usually not necessary either. A “verbatim” transcription, “as required for the expert assignment”, is entirely sufficient according to the landmark BGH ruling of 1999 (para. 56). The same applies to research interviews and journalistic conversations: the content counts, not the vernacular spelling.
In our view the court primarily addresses the analysis of content. For the identification of reality criteria, the exact vernacular wording should rarely matter, because this analysis step concerns the content (and not the paraverbal aspects) of a statement. What matters is therefore what was said; not how it was pronounced with a regional colouring.
How memozero supports you in practice
Because dialect errors can never be avoided completely on the technical side, we built memozero so that you spend as little time as possible on correction:
- Synchronous playback: Text and audio run in sync in the editor. You hear every unclear place in the original and correct it directly in the text, without searching in the audio.
- Marking of uncertain places: memozero shows you for each word how sure the model was. Uncertainties caused by dialect then stand out immediately.
- Individual dictionary: Add regional words, specialist terms and proper names that recur in your recordings. The results then improve over time.
- Automatic speaker separation: The speaker assignment works independently of the dialect, because it is based on voice features, not on what was said.
Incidentally, recording quality also plays a large role. A closely placed microphone and little background noise help the model in general.
Our conclusion
An AI that writes every German dialect without error and true to the dialect does not exist at present, and that will not change in the short term. For reliable working results it is also not required: a verbatim, complete transcription with an efficient way to correct it covers the requirements of practice.
We therefore recommend that you try memozero for 14 days with your own audio material. You deposit a means of payment when you subscribe. If you cancel before the end of the 14th day, you pay nothing. After that the subscription automatically continues into the paid period. The details are in Subscribe and manage your plan. If you are not satisfied, please tell us what we should improve or add from your point of view.