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Dialect, Accent, and Non-Standard Language: How Transcription Handles Them

· 5 min read ·

Transcription models train on standard language — and most speakers do not speak standard language precisely. A regional accent, a strong dialect, non-native pronunciation. Every speaker is a slightly different case. Where the limits of today's models lie and what can realistically be done about it.

Why Models Prefer Standard Language

Transcription models learn from data. Training data reflects the language available on the internet in audio or text form: podcasts, YouTube, television programmes, conferences, media transcripts. This content is produced predominantly in standard or near-standard language — speakers in media and on public platforms consciously or unconsciously standardize their pronunciation.

Dialectal recordings in available data: dialectological research, regional programmes, oral history recordings. Their share of total training data volume is marginal. The result is straightforward: the model learns standard language well and dialectal variants poorly — not because it is poorly designed, but because it lacks sufficient examples from dialectal regions.

Phonetic Distance as a Predictor of Results

The more a speaker's phonetics differ from standard pronunciation, the worse the model handles their speech. Phonetic distance is measurable in linguistics — but for a practical estimate, an intuitive scale suffices:

Specific Challenges for Different Types of Deviation

Regional Dialects

Some dialects have characteristic phonetic features: systematic vowel shifts, consonant changes, or altered stress patterns. These substitutions are regular and systematic — the model hears a different sound than in its training data and transcribes phonetically what it receives.

Result: the model transcribes the dialectal expression into the nearest standard variant or produces a word that does not exist. For researchers capturing dialectal data, verbatim transcription of dialectal forms from automation is unreliable — the model systematically "corrects" dialectal pronunciation towards the standard.

Non-Native Accent

A non-native speaker carries phonetic patterns from their first language: altered stress placement, different vowel qualities, specific intonation patterns. When phonemic differences are relatively small, models cope with this type of accent better than with strong dialects.

Strong Foreign Accent

Phonetic distance is significant: vowels, consonant clusters, stress placement, intonation — all different. Word error rate rises with the degree of accent. For heavily accented speech with a strong foreign accent, results can be very unreliable.

Colloquial Language — the Non-Standard Default

Colloquial, informal speech is non-standard but better represented in transcription training data than regional dialects — it appears commonly in YouTube videos, podcasts, and online content. Models are somewhat familiar with it: informal contractions and colloquialisms may appear correctly in transcripts.

The problem: the model is not consistent. Sometimes it preserves the non-standard form, other times it "corrects" it to standard — without a predictable pattern. For verbatim transcription of spontaneous colloquial speech, checking whether the model preserved the non-standard variant or normalized it is necessary.

How to Improve Accuracy for Dialects and Accents

Ensemble Approach as a Partial Solution

Different models have different training data and different strengths. For a particular type of accent or dialect, one model may perform better than another — without a predictable pattern, because the composition of training data across models is not transparently documented.

The merging layer selects between model variants: if multiple models agree on a particular transcription, that variant is more likely correct. For dialectal data, the ensemble does not help if all models fail on the same phenomenon — and for strong dialects, that is probable. A13

Custom Glossary for Regional Terms

Specific dialectal or regional expressions can be added to a custom glossary as preferred variants. If the researcher knows the respondent uses regional terms, they include them in the glossary. The model then prefers these variants when making decisions.

Limitation: the glossary influences the choice of specific words included in the list. It does not affect the phonetic mapping of the entire audio track — pronunciation patterns of dialects remain problematic even with a glossary.

Human Transcriber for Research Needs

For dialectological or sociolinguistic research where the dialectal form is part of the data under analysis, automation is insufficient as the primary tool. Automatic transcription can serve as an initial draft — but fidelity to the dialectal form must be guaranteed by a human transcriber with knowledge of the given dialect.

Realistic model: automatic transcription saves 50-60% of transcription time even for dialectal recordings. The remaining 40-50% is systematic editing of dialectal forms that the model normalized.

What Dialectal Transcription Tells Us About the Future of Models

Progress in accuracy for dialectal speech depends on the existence of training data — and such data is collected slowly.

Collecting and annotating dialectal data is costly: it requires fieldwork, specialized linguists, and agreed-upon conventions. Commercial motivation is low because the market for dialect transcription is small. Academic projects exist — spoken language corpora, oral history archives — but their scale is limited compared to commercial datasets used for model training.

The result is a structural problem without an easy solution: until sufficient dialectal training data exists, models will prefer the standard. And until the market demands dialectal transcription in larger volumes, the economic motivation to collect such data will remain low.

Czech Transcription System processes recordings through multiple models of different origin. For strongly dialectal or accented recordings, the ensemble approach can help where one model fails and another succeeds — but no guarantee is possible. For transcription of dialectal or heavily accented speech, it is realistic to expect a higher volume of manual editing than for standard speech.

Why language morphology, inflection, and word length present special challenges for transcription models is explained in the basic language overview A02. How word error rate reflects the impact of dialect on accuracy is described in the accuracy metrics overview A07.

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