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A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English

Paper · 2026 · Dana Serditova and Kevin Tang

Summary

Commercial speech recognisers handle regional accents worse than mainstream ones. Earlier work had rarely identified which dialect features cause the failures or how error rates differ among speakers of the same community. This paper studies the problem using Newcastle (Tyneside) English. The authors ran spontaneous sociolinguistic interview speech from the DECTE corpus through Rev AI, a commercial ASR system. They then hand-classified more than 3,000 transcription errors as phonological, morphosyntactic, lexical or standardisation errors. A negative binomial mixed-effects model related error counts to error type and to speaker gender, age and socioeconomic status. An acoustic case study then compared vowel formant trajectories between speakers with high, average and low error counts. Most errors were phonological, tied to local vowel quality and glottalisation. Others came from dialect vocabulary and non-standard grammar. Men had more errors, especially lexical ones, and so did the youngest and oldest speakers. The paper concludes that ASR errors follow social patterns rather than random ones. It argues that evaluation should go beyond aggregate accuracy and draw on sociolinguistic expertise.

Key points

  • Data: unscripted interview recordings drawn from DECTE, the Diachronic Electronic Corpus of Tyneside English. The system evaluated is Rev AI.
  • Over 3,000 errors were coded into four domains: phonological, morphosyntactic, lexical and standardisation. Phonological errors were the most frequent.
  • The recurrent problem features were Newcastle vowel realisations, glottalisation, local lexicon and non-standard grammatical forms.
  • In the negative binomial mixed-effects model, error type and gender were significant. Male speakers showed especially high lexical error rates, and speakers at both ends of the age range also fared worse.
  • Phonological features were first coded by ear, then checked with formant-trajectory analysis. Speakers with gradient vowel differences showed different error levels. Data and scripts are promised on OSF after publication.

Interesting ideas

  • I1. Error analysis is split into linguistic domains (sound, grammar, words, spelling conventions) rather than reported as one score, so each failure points to a specific linguistic cause.
  • I2. Errors are broken down by subgroups of speakers within one community, which reveals biases that a corpus-wide average hides.
  • I3. Categorical labels come first and gradient measurement second: features are first coded as present or absent, then measured continuously to show that the degree of deviation matters too.
  • I4. Non-standard grammatical forms and local vocabulary are a distinct error source alongside pronunciation.
  • I5. Error counts are modelled with a count-based mixed-effects model that includes speaker-level variation, rather than by comparing raw percentages.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Mova's evaluation reports could tag every wrong answer by the layer that broke (lemma, attachment, role, world state) instead of giving only overall accuracy.
  • I2 (learn): parser and reader scores could be split by text source, register or author group, so we can see whether silver data favours "standard" prose.
  • I3 (tree): parse failures could be scored not only by whether a construction is non-standard but by how far it departs from the treebank norm, to prioritise conversion rules.
  • I4 (tokens): dialectal or informal word forms could get explicit lemma and morphology entries so they do not silently become unknown or mis-tagged tokens downstream.
  • I5 (learn): when comparing parser variants or teacher tiers, a per-document random effect would separate real gains from a few unusually hard texts.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Heinrich Heine University DüsseldorfDüsseldorf · Germany34 works · 1,487 citations🎓University of FloridaGainesville · United States36 works · 364 citations🎓University of RegensburgRegensburg · Germany5 works · 14 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,379 worksSpeech & spoken language · 519 works

Cite

Cite as Dana Serditova and Kevin Tang. 2026. A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English. arXiv:2603.24549.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2603.24549 · arXiv 2603.24549

@misc{serditova2026sociolinguistic,
  title = {{A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English}},
  author = {Dana Serditova and Kevin Tang},
  year = {2026},
  doi = {10.48550/arxiv.2603.24549},
  eprint = {2603.24549},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2603.24549}
}

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