---
title: "A Sociolinguistic Analysis of Automatic Speech Recognition Bias"
description: "This paper studies the problem using Newcastle (Tyneside) English. 100 references. Among them: 0."
authors: ["Dana Serditova", "Kevin Tang"]
year: 2026
venue: "arXiv:2603.24549"
source: "https://arxiv.org/abs/2603.24549"
doi: "10.48550/arxiv.2603.24549"
arxiv: "2603.24549"
licence: "CC BY-NC-ND 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/serditova-2026-sociolinguistic-analysis-automatic-speech-recognition/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/serditova-2026-sociolinguistic-analysis-automatic-speech-recognition/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/20/) / A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English

# A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English

**Authors:** [Dana Serditova](https://md.research.elamur.ai/free/en/authors/dana-serditova/) and [Kevin Tang](https://md.research.elamur.ai/free/en/authors/kevin-tang/) · **Year:** 2026 · **Venue:** arXiv:2603.24549 · **Licence:** [CC BY-NC-ND 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2603.24549) · [PDF](https://arxiv.org/pdf/2603.24549) · [DOI 10.48550/arxiv.2603.24549](https://doi.org/10.48550/arxiv.2603.24549) · [arXiv 2603.24549](https://arxiv.org/abs/2603.24549) · [OpenAlex](https://openalex.org/W7140628103)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## 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
- 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.
- Errors are broken down by subgroups of speakers within one community, which reveals biases that a corpus-wide average hides.
- 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.
- Non-standard grammatical forms and local vocabulary are a distinct error source alongside pronunciation.
- 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 Düsseldorf](https://md.research.elamur.ai/free/en/universities/heinrich-heine-university-dusseldorf/) — University, Düsseldorf, Germany · 34 works · 1,487 citations
- [University of Florida](https://md.research.elamur.ai/free/en/universities/university-of-florida/) — University, Gainesville, United States · 36 works · 364 citations
- [University of Regensburg](https://md.research.elamur.ai/free/en/universities/university-of-regensburg/) — University, Regensburg, Germany · 5 works · 14 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 930 works

🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,379 works · [🎙️ Speech & spoken language](https://md.research.elamur.ai/free/en/topics/speech/) · 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.

```bibtex
@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}
}
```

- [← previous A Single Model Ensemble Framework for Neural Machine Translation Using Pivot Translation](https://md.research.elamur.ai/free/en/papers/oh-2026-single-model-ensemble-framework-neural/)
- [next → A Sociophonetic Analysis of Racial Bias in Commercial ASR Systems Using the Pacific Northwest English Corpus](https://md.research.elamur.ai/free/en/papers/scott-2026-sociophonetic-analysis-racial-bias-commercial/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/20/)

## References

100 references, 10 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Adedeji, A., Joshi, S., and Doohan, B. (2024). The Sound of Healthcare: Improving medical transcription ASR accuracy with large language models. [↗](https://doi.org/10.48550/arxiv.2402.07658)
2. Bassignana, E., Curry, A. C., and Hovy, D. (2025). [The AI gap: How socioeconomic status affects language technology interactions](https://md.research.elamur.ai/free/en/papers/bassignana-2025-ai-gap-how-socioeconomic-status/). In Che, W., Nabende, J., Shutova, E., and Pilehvar, M. T., editors, Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18647–18664, Vienna, Austria. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2505.12158)
3. Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). [Fitting linear mixed-effects models using lme4](https://md.research.elamur.ai/free/en/papers/bates-2015-fitting-linear-mixed-effects-models/). Journal of Statistical Software, 67(1):1– 48. [↗](https://doi.org/10.18637/jss.v067.i01)
4. Bauernfeind, L., Ahrens, C., Grama, J., Skarnitzl, R., and Volín, J. (2023). Change across the Lifespan in GOAT: Evidence from a panel study of Tyneside English. In Proceedings of the 20th International Congress of Phonetic Science. Guarant International, pages 2064–2068. [find](https://scholar.google.com/scholar?q=Bauernfeind%2C+L%2E%2C+Ahrens%2C+C%2E%2C+Grama%2C+J%2E%2C+Skarnitzl%2C+R%2E%2C+and+Vol%C3%ADn%2C+J%2E+%282023%29%2E+Change+across+the+Lifespan+in+GOAT%3A+Evidence+from+a+panel+study+of+Tyneside+English%2E+In+Proceedings+of+the+20th+Internation)
5. Beal, J. (1993). The Grammar of Tyneside and Northumbrian English. In Real English, pages 187–213. Routledge. [find](https://scholar.google.com/scholar?q=Beal%2C+J%2E+%281993%29%2E+The+Grammar+of+Tyneside+and+Northumbrian+English%2E+In+Real+English%2C+pages+187%E2%80%93213%2E+Routledge%2E)
6. Beal, J. (2004). English Dialects in the North of England: Phonology. A Handbook of Varieties of English, 1:113–133. [find](https://scholar.google.com/scholar?q=Beal%2C+J%2E+%282004%29%2E+English+Dialects+in+the+North+of+England%3A+Phonology%2E+A+Handbook+of+Varieties+of+English%2C+1%3A113%E2%80%93133%2E)
7. Bera, A. and Agarwal, A. (2025). Bias detection and mitigation framework for asr system. In Wang, C.-C. and Sangalang, R. G. B., editors, 7th International Conference on Signal Processing and Information Communications, pages 13–27, Cham. Springer Nature Switzerland. [find](https://scholar.google.com/scholar?q=Bera%2C+A%2E+and+Agarwal%2C+A%2E+%282025%29%2E+Bias+detection+and+mitigation+framework+for+asr+system%2E+In+Wang%2C+C%2E%2DC%2E+and+Sangalang%2C+R%2E+G%2E+B%2E%2C+editors%2C+7th+International+Conference+on+Signal+Processing+and+Informat)
8. Butler, J., Trager, B., and Behm, B. (2019). Exploration of Automatic Speech Recognition for Deaf and Hard of Hearing Students in Higher Education Classes. In Proceedings of the 21st International ACM SIGACCESS Conference on Computers and Accessibility, pages 32–42. [find](https://scholar.google.com/scholar?q=Butler%2C+J%2E%2C+Trager%2C+B%2E%2C+and+Behm%2C+B%2E+%282019%29%2E+Exploration+of+Automatic+Speech+Recognition+for+Deaf+and+Hard+of+Hearing+Students+in+Higher+Education+Classes%2E+In+Proceedings+of+the+21st+International+ACM)
9. Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.-F., Brañas-Garza, P., Butera, L., Douglas, K. M., et al. (2024). The Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy Making. PNAS nexus, 3(6):pgae191. [↗](https://doi.org/10.1093/pnasnexus/pgae191)
10. Carr, P. (1999). Sociophonetic variation and generative phonology: the case of tyneside english. Cahiers de grammaire, 24:7–15. [find](https://scholar.google.com/scholar?q=Carr%2C+P%2E+%281999%29%2E+Sociophonetic+variation+and+generative+phonology%3A+the+case+of+tyneside+english%2E+Cahiers+de+grammaire%2C+24%3A7%E2%80%9315%2E)
11. Carr, P. (2019). English phonetics and phonology: An introduction. John Wiley & Sons. [find](https://scholar.google.com/scholar?q=Carr%2C+P%2E+%282019%29%2E+English+phonetics+and+phonology%3A+An+introduction%2E+John+Wiley+%26+Sons%2E)
12. Choi, A. S. G. and Choi, H. (2025). Fairness of automatic speech recognition: Looking through a philosophical lens. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(1):605–614. [find](https://scholar.google.com/scholar?q=Choi%2C+A%2E+S%2E+G%2E+and+Choi%2C+H%2E+%282025%29%2E+Fairness+of+automatic+speech+recognition%3A+Looking+through+a+philosophical+lens%2E+Proceedings+of+the+AAAI%2FACM+Conference+on+AI%2C+Ethics%2C+and+Society%2C+8%281%29%3A605%E2%80%93614%2E)
13. Clopper, C. G. (2021). Perception of Dialect Variation, chapter 13, pages 333–364. John Wiley & Sons, Ltd. [find](https://scholar.google.com/scholar?q=Clopper%2C+C%2E+G%2E+%282021%29%2E+Perception+of+Dialect+Variation%2C+chapter+13%2C+pages+333%E2%80%93364%2E+John+Wiley+%26+Sons%2C+Ltd%2E)
14. Corrigan, K. P., Buchstaller, I., Mearns, A., and Moisl, H. (2012). The Diachronic Electronic Corpus of Tyneside English. Online at https://research.ncl.ac.uk/decte/index.htm (Accessed April 2, 2024). [↗](https://doi.org/10.25405/ncl.34247331)
15. Cox, F., Penney, J., and Palethorpe, S. (2024). Australian English Monophthong Change Across 50 Years: Static versus dynamic measures. Languages, 9(3):99. [↗](https://doi.org/10.3390/languages9030099)
16. Cumbal, R., Moell, B., Lopes, J., and Engwall, O. (2024). You Don’t Understand Me!: Comparing ASR Results for L1 and L2 Speakers of Swedish. arXiv preprint arXiv:2405.13379. [↗](https://arxiv.org/abs/2405.13379)
17. DiChristofano, A., Shuster, H., Chandra, S., and Patwari, N. (2022). [Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition](https://md.research.elamur.ai/free/en/papers/dichristofano-2022-global-performance-disparities-between-english/). arXiv preprint arXiv:2208.01157. [↗](https://arxiv.org/abs/2208.01157)
18. Dipto, T. T., Hossain, A., Faruque, R. S., Hassan, M. R., Fatema, K., Shome, T., Naswan, R., Zihad, M., Anam, M. U., Tasnim, N., Mahmud, H., Hasan, M. K., Shawon, M. M. H., Sadeque, F., and Reasat, T. (2025). Are ASR foundation models generalized enough to capture features of regional dialects for low-resource languages? In Inui, K., Sakti, S., Wang, H., Wong, D. F., Bhattacharyya, P., Banerjee, B., Ekbal, A., Chakraborty, T., and Singh, D. P., editors, Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 178–188, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2510.23252)
19. Docherty, G. and Foulkes, P. (1999). Sociophonetic Variation in ‘Glottals’ in Newcastle English. In Proceedings of the 14th International Congress of Phonetic Sciences, pages 1037–1040. University of California, Berkeley. [find](https://scholar.google.com/scholar?q=Docherty%2C+G%2E+and+Foulkes%2C+P%2E+%281999%29%2E+Sociophonetic+Variation+in+%E2%80%98Glottals%E2%80%99+in+Newcastle+English%2E+In+Proceedings+of+the+14th+International+Congress+of+Phonetic+Sciences%2C+pages+1037%E2%80%931040%2E+University+of+)
20. Dong, J. (2024). Fairness, Relationship, and Identity Construction in Human–AI Interaction. Journal of Sociolinguistics, 28(5). [↗](https://doi.org/10.1111/josl.12687)
21. Feng, S., Halpern, B. M., Kudina, O., and Scharenborg, O. (2024). Towards Inclusive Automatic Speech Recognition. Computer Speech & Language, 84:101567. [↗](https://doi.org/10.1016/j.csl.2023.101567)
22. Foster, M. E. and Stuart-Smith, J. (2023). Social Robotics Meets Sociolinguistics: Investigating accent bias and social context in HRI. In Companion of the 2023 ACM/IEEE international conference on human-robot interaction, pages 156–160. [find](https://scholar.google.com/scholar?q=Foster%2C+M%2E+E%2E+and+Stuart%2DSmith%2C+J%2E+%282023%29%2E+Social+Robotics+Meets+Sociolinguistics%3A+Investigating+accent+bias+and+social+context+in+HRI%2E+In+Companion+of+the+2023+ACM%2FIEEE+international+conference+on+hu)
23. Fox, J. and Monette, G. (1992). Generalized collinearity diagnostics. Journal of the American Statistical Association, 87(417):178–183. [↗](https://doi.org/10.1080/01621459.1992.10475190)
24. Fruehwald, J. (2024). Working with the Discrete Cosine Transform in R. [↗](https://doi.org/10.59350/cqt3b-hx262)
25. Garnerin, M., Rossato, S., and Besacier, L. (2021). Investigating the impact of gender representation in ASR training data: a case study on librispeech. In Costa-jussà, M. R., Gonen, H., Hardmeier, C., and Webster, K., editors, Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, pages 86–92, Online. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Garnerin%2C+M%2E%2C+Rossato%2C+S%2E%2C+and+Besacier%2C+L%2E+%282021%29%2E+Investigating+the+impact+of+gender+representation+in+ASR+training+data%3A+a+case+study+on+librispeech%2E+In+Costa%2Djuss%C3%A0%2C+M%2E+R%2E%2C+Gonen%2C+H%2E%2C+Hardmeier%2C+C%2E)
26. Grama, J., Eiswirth, M. E., Buchstaller, I., Skarnitzl, R., and Volín, J. (2023a). Tracking Creak from Early to Late Adulthood: A panel study from the North East of England. In Proceedings of the 20th International Congress of Phonetic Science. Guarant International, pages 2049–2053. [find](https://scholar.google.com/scholar?q=Grama%2C+J%2E%2C+Eiswirth%2C+M%2E+E%2E%2C+Buchstaller%2C+I%2E%2C+Skarnitzl%2C+R%2E%2C+and+Vol%C3%ADn%2C+J%2E+%282023a%29%2E+Tracking+Creak+from+Early+to+Late+Adulthood%3A+A+panel+study+from+the+North+East+of+England%2E+In+Proceedings+of+the+20th)
27. Grama, J., Mechler, J., Bauernfeind, L., Eiswirth, M. E., and Buchstaller, I. (2023b). Post-Educator Relaxation in the U-shaped Curve: Evidence from a panel study of Tyneside (ing). Language Variation and Change, 35(3):325–350. [↗](https://doi.org/10.1017/s0954394523000194)
28. Green, J. R., MacDonald, R. L., Jiang, P.-P., Cattiau, J., Heywood, R., Cave, R., Seaver, K., Ladewig, M. A., Tobin, J., Brenner, M. P., et al. (2021). Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases. In Interspeech, volume 2021, pages 4778–4782. [find](https://scholar.google.com/scholar?q=Green%2C+J%2E+R%2E%2C+MacDonald%2C+R%2E+L%2E%2C+Jiang%2C+P%2E%2DP%2E%2C+Cattiau%2C+J%2E%2C+Heywood%2C+R%2E%2C+Cave%2C+R%2E%2C+Seaver%2C+K%2E%2C+Ladewig%2C+M%2E+A%2E%2C+Tobin%2C+J%2E%2C+Brenner%2C+M%2E+P%2E%2C+et+al%2E+%282021%29%2E+Automatic+Speech+Recognition+of+Disordered+Speec)
29. Grieve, J., Bartl, S., Fuoli, M., Grafmiller, J., Huang, W., Jawerbaum, A., Murakami, A., Perlman, M., Roemling, D., and Winter, B. (2025). The Sociolinguistic Foundations of Language Modeling. Frontiers in Artificial Intelligence, 7:1472411. [↗](https://doi.org/10.3389/frai.2024.1472411)
30. Gut, U. (2009). Introduction to English phonetics and phonology, volume 1. Peter Lang. [find](https://scholar.google.com/scholar?q=Gut%2C+U%2E+%282009%29%2E+Introduction+to+English+phonetics+and+phonology%2C+volume+1%2E+Peter+Lang%2E)
31. Guy, G. R. (1988). Language and Social Class. Linguistics: The Cambridge Survey, 4:37–63. [↗](https://doi.org/10.1017/cbo9780511620577.004)
32. Halpern, B. M., Feng, S., van Son, R., van den Brekel, M., and Scharenborg, O. (2022). Low-Resource Automatic Speech Recognition and Error Analyses of Oral Cancer Speech. Speech Communication, 141:14–27. [↗](https://doi.org/10.1016/j.specom.2022.04.006)
33. Hämäläinen, A., Teixeira, A., Almeida, N., Meinedo, H., Fegyó, T., and Dias, M. S. (2015). Multilingual Speech Recognition for the Elderly: The AALFred personal life assistant. Procedia Computer Science, 67:283–292. [find](https://scholar.google.com/scholar?q=H%C3%A4m%C3%A4l%C3%A4inen%2C+A%2E%2C+Teixeira%2C+A%2E%2C+Almeida%2C+N%2E%2C+Meinedo%2C+H%2E%2C+Fegy%C3%B3%2C+T%2E%2C+and+Dias%2C+M%2E+S%2E+%282015%29%2E+Multilingual+Speech+Recognition+for+the+Elderly%3A+The+AALFred+personal+life+assistant%2E+Procedia+Computer+Scien)
34. Heuser, A., Kendall, T., del Rio, M., McNamara, Q., Bhandari, N., Miller, C., and Jetté, M. (2024). [Quantification of stylistic differences in human-and ASR-produced transcripts of African American English](https://md.research.elamur.ai/free/en/papers/heuser-2024-quantification-stylistic-differences-human-asr/). In Interspeech 2024, pages 4538–4542.
35. Hickey, R. (2015). The North of England and Northern English. In Researching Northern English, pages 1–24. John Benjamins. [↗](https://doi.org/10.1075/veaw.g55.01hic)
36. Hughes, A., Trudgill, P., and Watt, D. (2013). English Accents and Dialects: An introduction to social and regional varieties of English in the British Isles. Routledge. [find](https://scholar.google.com/scholar?q=Hughes%2C+A%2E%2C+Trudgill%2C+P%2E%2C+and+Watt%2C+D%2E+%282013%29%2E+English+Accents+and+Dialects%3A+An+introduction+to+social+and+regional+varieties+of+English+in+the+British+Isles%2E+Routledge%2E)
37. Jahan, M., Mazumdar, P., Thebaud, T., Hasegawa-Johnson, M., Villalba, J., Dehak, N., and Moro-Velazquez, L. (2025). Unveiling Performance Bias in ASR Systems: A Study on Gender, Age, Accent, and More. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE. [find](https://scholar.google.com/scholar?q=Jahan%2C+M%2E%2C+Mazumdar%2C+P%2E%2C+Thebaud%2C+T%2E%2C+Hasegawa%2DJohnson%2C+M%2E%2C+Villalba%2C+J%2E%2C+Dehak%2C+N%2E%2C+and+Moro%2DVelazquez%2C+L%2E+%282025%29%2E+Unveiling+Performance+Bias+in+ASR+Systems%3A+A+Study+on+Gender%2C+Age%2C+Accent%2C+and+More%2E)
38. Jitsi (2020). jiwer: Speech recognition evaluation in python. [https://gith](https://gith) ub.com/jitsi/jiwer. [↗](https://gith)
39. Johnson, A., Shankar, N. B., Ostendorf, M., and Alwan, A. (2024a). An Exploratory Study on Dialect Density Estimation for Children and Adult’s African American English. The Journal of the Acoustical Society of America, 155(4):2836–2848. [↗](https://doi.org/10.1121/10.0025771)
40. Johnson, A., Shankar, N. B., Ostendorf, M., and Alwan, A. (2024b). An exploratory study on dialect density estimation for children and adult’s african american english). The Journal of the Acoustical Society of America, 155(4):2836–2848. [↗](https://doi.org/10.1121/10.0025771)
41. Kelly-Holmes, H. (2024). Artificial Intelligence and the Future of Our Sociolinguistic Work. Journal of Sociolinguistics, 28(5):3–10. [↗](https://doi.org/10.1111/josl.12678)
42. Kendall, T., Vaughn, C., Farrington, C., Gunter, K., McLean, J., Tacata, C., and Arnson, S. (2021). Considering performance in the automated and manual coding of sociolinguistic variables: Lessons from variable (ING). Frontiers in Artificial Intelligence, Volume 4 - 2021. [↗](https://doi.org/10.3389/frai.2021.648543)
43. Kessler, J. (2017). Scattertext: A Browser-Based Tool for Visualizing How Corpora Differ. In Proceedings of ACL 2017, system demonstrations, pages 85–90. [find](https://scholar.google.com/scholar?q=Kessler%2C+J%2E+%282017%29%2E+Scattertext%3A+A+Browser%2DBased+Tool+for+Visualizing+How+Corpora+Differ%2E+In+Proceedings+of+ACL+2017%2C+system+demonstrations%2C+pages+85%E2%80%9390%2E)
44. Kessler, M. and Casal, J. E. (2024). (Socio) linguistics and Generative AI: Taking the Reins as Researchers and Steering Its Use Toward Ethical Outcomes. Journal of Sociolinguistics, 28(5). [↗](https://doi.org/10.1111/josl.12682)
45. Kim, C. and Stern, R. M. (2008). Robust Signal-to-noise Ratio Estimation Based on Waveform Amplitude Distribution Analysis. In Ninth Annual Conference of the International Speech Communication Association. [find](https://scholar.google.com/scholar?q=Kim%2C+C%2E+and+Stern%2C+R%2E+M%2E+%282008%29%2E+Robust+Signal%2Dto%2Dnoise+Ratio+Estimation+Based+on+Waveform+Amplitude+Distribution+Analysis%2E+In+Ninth+Annual+Conference+of+the+International+Speech+Communication+Associa)
46. Klakow, D. and Peters, J. (2002). Testing the Correlation of Word Error Rate and Perplexity. Speech Communication, 38(1-2):19–28. [↗](https://doi.org/10.1016/s0167-6393(01)00041-3)
47. Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J. R., Jurafsky, D., and Goel, S. (2020). Racial Disparities in Automated Speech Recognition. Proceedings of the National Academy of Sciences, 117(14):7684–7689. [↗](https://doi.org/10.1073/pnas.1915768117)
48. Labov, W. (1963). 19(3):273–309. The Social Motivation of a Sound Change. [find](https://scholar.google.com/scholar?q=Labov%2C+W%2E+%281963%29%2E+19%283%29%3A273%E2%80%93309%2E+The+Social+Motivation+of+a+Sound+Change%2E)
49. Labov, W. (1972). Language in the Inner City: Studies in the Black English Vernacular, volume 3. University of Pennsylvania Press. [find](https://scholar.google.com/scholar?q=Labov%2C+W%2E+%281972%29%2E+Language+in+the+Inner+City%3A+Studies+in+the+Black+English+Vernacular%2C+volume+3%2E+University+of+Pennsylvania+Press%2E)
50. Labov, W. (1986). The Social Stratification of (r) in New York City Department Stores. In Dialect and language variation, pages 304–329. Elsevier. [↗](https://doi.org/10.1016/b978-0-12-051130-3.50029-x)
51. Lai, L.-F. and Holliday, N. R. (2023). Exploring Sources of Racial Bias in Automatic Speech Recognition through the Lens of Rhythmic Variation. In INTERSPEECH, volume 2023, pages 1284–1288. [find](https://scholar.google.com/scholar?q=Lai%2C+L%2E%2DF%2E+and+Holliday%2C+N%2E+R%2E+%282023%29%2E+Exploring+Sources+of+Racial+Bias+in+Automatic+Speech+Recognition+through+the+Lens+of+Rhythmic+Variation%2E+In+INTERSPEECH%2C+volume+2023%2C+pages+1284%E2%80%931288%2E)
52. Lai, L.-F., van Hell, J. G., and Lipski, J. (2025). Dialect Bias in Automatic Speech Recognition: Analysis of Appalachian English. American Speech: A Quarterly of Linguistic Usage, 100(2):190–207. [↗](https://doi.org/10.1215/00031283-11466494)
53. Latif, S., Qadir, J., Qayyum, A., Usama, M., and Younis, S. (2020). Speech Technology for Healthcare: Opportunities, challenges, and state of the art. IEEE Reviews in Biomedical Engineering, 14:342–356. [↗](https://doi.org/10.1109/rbme.2020.3006860)
54. Lenth, R. V. and Piaskowski, J. (2025). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 2.0.1. [find](https://scholar.google.com/scholar?q=Lenth%2C+R%2E+V%2E+and+Piaskowski%2C+J%2E+%282025%29%2E+emmeans%3A+Estimated+Marginal+Means%2C+aka+Least%2DSquares+Means%2E+R+package+version+2%2E0%2E1%2E)
55. Lindsey, G. (2019). English after RP: Standard British pronunciation today. Springer. [find](https://scholar.google.com/scholar?q=Lindsey%2C+G%2E+%282019%29%2E+English+after+RP%3A+Standard+British+pronunciation+today%2E+Springer%2E)
56. Word, Liu, C., Picheny, M., Sarı, L., Chitkara, P., Xiao, A., Zhang, X., Chou, M., Alvarado, A., Hazirbas, C., and Saraf, Y. (2022). Towards Measuring Fairness in Speech Recognition: Casual conversations dataset transcriptions. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6162–6166. IEEE. [↗](https://doi.org/10.17023/p3bz-3f71)
57. Mallinson, C., Janeja, V. P., Evered, C., Khanjani, Z., Davis, L., Bhalli, N. N., and Nwosu, K. (2024). A Place for (Socio) Linguistics in Audio Deepfake Detection and Discernment: Opportunities for convergence and interdisciplinary collaboration. Language and Linguistics Compass, 18(5):e12527. [↗](https://doi.org/10.1111/lnc3.12527)
58. Markl, N. (2022). Language Variation and Algorithmic Bias: Understanding algorithmic bias in British English Automatic Speech Recognition. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 521–534. [find](https://scholar.google.com/scholar?q=Markl%2C+N%2E+%282022%29%2E+Language+Variation+and+Algorithmic+Bias%3A+Understanding+algorithmic+bias+in+British+English+Automatic+Speech+Recognition%2E+In+Proceedings+of+the+2022+ACM+Conference+on+Fairness%2C+Accoun)
59. Markl, N., Hall-Lew, L., and Lai, C. (2024). Language technologies as if people mattered: Centering communities in language technology development. In Calzolari, N., Kan, M.-Y., Hoste, V., Lenci, A., Sakti, S., and Xue, N., editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10085–10099, Torino, Italia. ELRA and ICCL. [find](https://scholar.google.com/scholar?q=Markl%2C+N%2E%2C+Hall%2DLew%2C+L%2E%2C+and+Lai%2C+C%2E+%282024%29%2E+Language+technologies+as+if+people+mattered%3A+Centering+communities+in+language+technology+development%2E+In+Calzolari%2C+N%2E%2C+Kan%2C+M%2E%2DY%2E%2C+Hoste%2C+V%2E%2C+Lenci%2C+A%2E%2C+)
60. Markl, N. and Lai, C. (2021). Context-Sensitive Evaluation of Automatic Speech Recognition: Considering user experience & language variation. In Proceedings of the first workshop on bridging human–computer interaction and natural language processing, pages 34–40. [find](https://scholar.google.com/scholar?q=Markl%2C+N%2E+and+Lai%2C+C%2E+%282021%29%2E+Context%2DSensitive+Evaluation+of+Automatic+Speech+Recognition%3A+Considering+user+experience+%26+language+variation%2E+In+Proceedings+of+the+first+workshop+on+bridging+human%E2%80%93com)
61. Martin, J. L. (2021). Spoken corpora data, automatic speech recognition, and bias against African American language: The case of habitual ’be’. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 284, New York, NY, USA. Association for Computing Machinery. [find](https://scholar.google.com/scholar?q=Martin%2C+J%2E+L%2E+%282021%29%2E+Spoken+corpora+data%2C+automatic+speech+recognition%2C+and+bias+against+African+American+language%3A+The+case+of+habitual+%E2%80%99be%E2%80%99%2E+In+Proceedings+of+the+2021+ACM+Conference+on+Fairness%2C+A)
62. Martin, J. L. and Tang, K. (2020). Understanding racial Disparities in Automatic Speech Recognition: The Case of Habitual "be". In Interspeech, pages 626–630. [find](https://scholar.google.com/scholar?q=Martin%2C+J%2E+L%2E+and+Tang%2C+K%2E+%282020%29%2E+Understanding+racial+Disparities+in+Automatic+Speech+Recognition%3A+The+Case+of+Habitual+%22be%22%2E+In+Interspeech%2C+pages+626%E2%80%93630%2E)
63. Max Planck Institute for Psycholinguistics (2025). ELAN (Version 7.0) [Computer software]. [find](https://scholar.google.com/scholar?q=Max+Planck+Institute+for+Psycholinguistics+%282025%29%2E+ELAN+%28Version+7%2E0%29+%5BComputer+software%5D%2E)
64. McAuliffe, M., Socolof, M., Mihuc, S., Wagner, M., and Sonderegger, M. (2017). Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi. In Proc. Interspeech 2017, pages 498–502. [find](https://scholar.google.com/scholar?q=McAuliffe%2C+M%2E%2C+Socolof%2C+M%2E%2C+Mihuc%2C+S%2E%2C+Wagner%2C+M%2E%2C+and+Sonderegger%2C+M%2E+%282017%29%2E+Montreal+Forced+Aligner%3A+Trainable+Text%2DSpeech+Alignment+Using+Kaldi%2E+In+Proc%2E+Interspeech+2017%2C+pages+498%E2%80%93502%2E)
65. Mearns, A. (2015). Tyneside. In Researching Northern English, pages 161– 182. John Benjamins Publishing Company. [find](https://scholar.google.com/scholar?q=Mearns%2C+A%2E+%282015%29%2E+Tyneside%2E+In+Researching+Northern+English%2C+pages+161%E2%80%93+182%2E+John+Benjamins+Publishing+Company%2E)
66. Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J., and Tuennerman, E. (2021). “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans. Frontiers in Artificial Intelligence, 4:725911. [↗](https://doi.org/10.3389/frai.2021.725911)
67. Milroy, J. and Milroy, L. (1993). Mechanisms of Change in Urban Dialects: The Role of Class, Social Network and Gender. International Journal of Applied Linguistics, 3(1):57–77. [↗](https://doi.org/10.1111/j.1473-4192.1993.tb00043.x)
68. Milroy, L. (1980). Language and Social Networks. Blackwell. [find](https://scholar.google.com/scholar?q=Milroy%2C+L%2E+%281980%29%2E+Language+and+Social+Networks%2E+Blackwell%2E)
69. Moelders, A.-M. (2025). Navigating the vernacular across the lifespan: a panel study of the phonetic realisation of the first-person singular possessive. English Language & Linguistics, 29(1):132–158. [↗](https://doi.org/10.1017/s1360674324000431)
70. Mojarad, H. and Tang, K. (2025). [Automatic speech recognition of African American English: Lexical and contextual effects](https://md.research.elamur.ai/free/en/papers/mojarad-2025-automatic-speech-recognition-african-american/). In Proc. Interspeech 2025, pages 3883–3887. ISCA. [↗](https://doi.org/10.17605/osf.io/qn6a2)
71. Montgomery, C. (2012). The Effect of Proximity in Perceptual Dialectology. Journal of Sociolinguistics, 16(5):638–668. [↗](https://doi.org/10.1111/josl.12003)
72. Moore, E. (2021). The Social Meaning of Syntax. Social meaning and linguistic variation: Theorizing the third wave, pages 54–79. [↗](https://doi.org/10.1017/9781108578684.003)
73. Moore, E. (2023). Socio-syntax: Exploring the Social Life of Grammar. Cambridge University Press. [find](https://scholar.google.com/scholar?q=Moore%2C+E%2E+%282023%29%2E+Socio%2Dsyntax%3A+Exploring+the+Social+Life+of+Grammar%2E+Cambridge+University+Press%2E)
74. Mubarak, F., Suomi, R., and Kantola, S.-P. (2020). Confirming the Links Between Socio-Economic Variables and Digitalization Worldwide: The unsettled debate on digital divide. Journal of Information, Communication and Ethics in Society, 18(3):415–430. [↗](https://doi.org/10.1108/jices-02-2019-0021)
75. Ngueajio, M. K. and Washington, G. (2022). Hey ASR System! Why Aren’t You More Inclusive? Automatic Speech Recognition Systems’ Bias and Proposed Bias Mitigation Techniques. A Literature Review. In International Conference on Human-Computer Interaction, pages 421–440. Springer. [find](https://scholar.google.com/scholar?q=Ngueajio%2C+M%2E+K%2E+and+Washington%2C+G%2E+%282022%29%2E+Hey+ASR+System%21+Why+Aren%E2%80%99t+You+More+Inclusive%3F+Automatic+Speech+Recognition+Systems%E2%80%99+Bias+and+Proposed+Bias+Mitigation+Techniques%2E+A+Literature+Review%2E+In+In)
76. Nguyen, D. (2025). Collaborative growth: When large language models meet sociolinguistics. Language and Linguistics Compass, 19(2):e70010. [↗](https://doi.org/10.1111/lnc3.70010)
77. Nichols, P. (1983). Linguistic Options and Choices for Black Women in the Rural South. Language, Gender and Society, pages 54–68. [find](https://scholar.google.com/scholar?q=Nichols%2C+P%2E+%281983%29%2E+Linguistic+Options+and+Choices+for+Black+Women+in+the+Rural+South%2E+Language%2C+Gender+and+Society%2C+pages+54%E2%80%9368%2E)
78. Oppermann, S. and Siebenhaar, B. (2023). What’s That Phthong? Automated Classification of Dialectal Mono-and Standard Diphthongs. In Proceedings of the 20th International Congress of Phonetic Sciences, Prague, Czech Republic, pages 3637–3642. [find](https://scholar.google.com/scholar?q=Oppermann%2C+S%2E+and+Siebenhaar%2C+B%2E+%282023%29%2E+What%E2%80%99s+That+Phthong%3F+Automated+Classification+of+Dialectal+Mono%2Dand+Standard+Diphthongs%2E+In+Proceedings+of+the+20th+International+Congress+of+Phonetic+Sciences)
79. Pearce, M. (2012). Folk Accounts of Dialect Differences in Tyne and Wear. Dialectologia et Geolinguistica, 20(1):5–25. [↗](https://doi.org/10.1515/dialect-2012-0001)
80. Porwal, R., Rozet, A., Houck, P., Gowda, J., Moeller, S., and Tang, K. (2025). [Analysis of LLM as a grammatical feature tagger for African American English](https://md.research.elamur.ai/free/en/papers/porwal-2025-analysis-llm-as-grammatical-feature/).
81. Previlon, W., Rozet, A., Gowda, J., Dyer, B., Tang, K., and Moeller, S. (2024). [Leveraging syntactic dependencies in disambiguation: the case of African American English](https://md.research.elamur.ai/free/en/papers/previlon-2024-leveraging-syntactic-dependencies-disambiguation-case/). In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024, Torino, Italia. ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL). accepted (Preprint: https://doi.org/10.31234/o sf.io/ph7q8). [↗](https://doi.org/10.31234/o)
82. R Core Team (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. [find](https://scholar.google.com/scholar?q=R+Core+Team+%282025%29%2E+R%3A+A+Language+and+Environment+for+Statistical+Computing%2E+R+Foundation+for+Statistical+Computing%2C+Vienna%2C+Austria%2E)
83. Reddy, S. and Stanford, J. N. (2015). Toward Completely Automated Vowel Extraction: Introducing DARLA. Linguistics Vanguard, 1(1):15–28. [↗](https://doi.org/10.1515/lingvan-2015-0002)
84. Rickford, J. R. (1996). Regional and Social Variation. Sociolinguistics and language teaching, pages 151–194. [↗](https://doi.org/10.1017/cbo9780511551185.009)
85. Roach, P. (2009). English phonetics and phonology paperback with audio CDs (2): A practical course. Cambridge university press. [find](https://scholar.google.com/scholar?q=Roach%2C+P%2E+%282009%29%2E+English+phonetics+and+phonology+paperback+with+audio+CDs+%282%29%3A+A+practical+course%2E+Cambridge+university+press%2E)
86. Rosenfelder, I., Fruehwald, J., Evanini, K., Seyfarth, S., Brickhouse, C., Gorman, K., Prichard, H., and Yuan, J. (2022). Fave: Forced alignment and vowel extraction. GPL-3.0 License. [find](https://scholar.google.com/scholar?q=Rosenfelder%2C+I%2E%2C+Fruehwald%2C+J%2E%2C+Evanini%2C+K%2E%2C+Seyfarth%2C+S%2E%2C+Brickhouse%2C+C%2E%2C+Gorman%2C+K%2E%2C+Prichard%2C+H%2E%2C+and+Yuan%2C+J%2E+%282022%29%2E+Fave%3A+Forced+alignment+and+vowel+extraction%2E+GPL%2D3%2E0+License%2E)
87. Sanabria, R., Bogoychev, N., Markl, N., Carmantini, A., Klejch, O., and Bell, P. (2023). [The Edinburgh International Accents of English Corpus: Towards the democratization of English ASR](https://md.research.elamur.ai/free/en/papers/sanabria-2023-edinburgh-international-accents-english-corpus/). In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE.
88. Santiago, H., Martin, J., Moeller, S., and Tang, K. (2022). [Disambiguation of morpho-syntactic features of African American English – the case of habitual be](https://md.research.elamur.ai/free/en/papers/santiago-2022-disambiguation-morpho-syntactic-features-african/). In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 70–75, Dublin, Ireland. Association for Computational Linguistics.
89. Schneider, E. W., Burridge, K., Kortmann, B., Mesthrie, R., and Upton, C. (2004). A Handbook of Varieties of English: A multimedia reference tool two volumes plus CD-ROM. De Gruyter Mouton. [find](https://scholar.google.com/scholar?q=Schneider%2C+E%2E+W%2E%2C+Burridge%2C+K%2E%2C+Kortmann%2C+B%2E%2C+Mesthrie%2C+R%2E%2C+and+Upton%2C+C%2E+%282004%29%2E+A+Handbook+of+Varieties+of+English%3A+A+multimedia+reference+tool+two+volumes+plus+CD%2DROM%2E+De+Gruyter+Mouton%2E)
90. Serditova, D. and Carmichael, K. (2025). Meet Me on Tomorrow by Your Mama’s House: A sociolinguistic investigation of phrasal constructions in New Orleans English. Lingua, 328:104040. [↗](https://doi.org/10.1016/j.lingua.2025.104040)
91. Serditova, D., Tang, K., and Steffens, J. (2025). [Automatic Speech Recognition Biases in Newcastle English: an Error Analysis](https://md.research.elamur.ai/free/en/papers/serditova-2025-automatic-speech-recognition-biases-newcastle/). In Interspeech 2025, pages 3204–3208. [↗](https://doi.org/10.17605/osf.io/t8yxk)
92. Székely, É., Miniota, J., and Hejná, M. M. (2025). Will AI Shape the Say We Speak? The emerging sociolinguistic influence of synthetic voices. In Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology, pages 335–340. [find](https://scholar.google.com/scholar?q=Sz%C3%A9kely%2C+%C3%89%2E%2C+Miniota%2C+J%2E%2C+and+Hejn%C3%A1%2C+M%2E+M%2E+%282025%29%2E+Will+AI+Shape+the+Say+We+Speak%3F+The+emerging+sociolinguistic+influence+of+synthetic+voices%2E+In+Proceedings+of+the+15th+International+Workshop+on+Spok)
93. Torgbi, M., Clayman, A., Speight, J. J., and Madabushi, H. T. (2025). Adapting Whisper for Regional Dialects: Enhancing Public Services for Vulnerable Populations in the United Kingdom. arXiv preprint arXiv:2501.08502. [↗](https://arxiv.org/abs/2501.08502)
94. Trudgill, P. (1972). Sex, Covert Prestige and Linguistic Change in the Urban British English of Norwich. Language in Society, 1(2):179–195. [↗](https://doi.org/10.1017/s0047404500000488)
95. Vacher, M., Fleury, A., Portet, F., Serignat, J.-F., and Noury, N. (2010). Complete Sound and Speech Recognition System for Health Smart Homes: Application to the recognition of activities of daily living. New Developments in Biomedical Engineering, pages pp–645. [↗](https://doi.org/10.5772/7596)
96. Wassink, A. B., Gansen, C., and Bartholomew, I. (2022). Uneven Success: Automatic Speech Recognition and Ethnicity-Related Dialects. Speech Communication, 140:50–70. [↗](https://doi.org/10.1016/j.specom.2022.03.009)
97. Watt, D. (2002). ‘I Don’t Speak with a Geordie Accent, I Speak, Like, the Northern Accent’: Contact-induced levelling in the Tyneside vowel system. Journal of Sociolinguistics, 6(1):44–63. [↗](https://doi.org/10.1111/1467-9481.00176)
98. Watt, D. and Milroy, L. (2014). Patterns of Variation and Change in Three Newcastle Vowels: Is this dialect levelling? In Urban Voices, pages 25–46. Routledge. [find](https://scholar.google.com/scholar?q=Watt%2C+D%2E+and+Milroy%2C+L%2E+%282014%29%2E+Patterns+of+Variation+and+Change+in+Three+Newcastle+Vowels%3A+Is+this+dialect+levelling%3F+In+Urban+Voices%2C+pages+25%E2%80%9346%2E+Routledge%2E)
99. Zou, Y., Liu, X., Xu, H., Hou, Y., and Qi, J. (2021). Design of Intelligent Customer Service Report System Based on Automatic Speech Recognition and Text Classification. In E3S Web of Conferences, volume 295, page 01064. EDP Sciences. [find](https://scholar.google.com/scholar?q=Zou%2C+Y%2E%2C+Liu%2C+X%2E%2C+Xu%2C+H%2E%2C+Hou%2C+Y%2E%2C+and+Qi%2C+J%2E+%282021%29%2E+Design+of+Intelligent+Customer+Service+Report+System+Based+on+Automatic+Speech+Recognition+and+Text+Classification%2E+In+E3S+Web+of+Conferences%2C+vol)
100. Zusag, M., Wagner, L., and Thallinger, B. (2024). Crisperwhisper: Accurate timestamps on verbatim speech transcriptions. In Interspeech 2024, pages 1265–1269. [↗](https://doi.org/10.48550/arxiv.2408.16589)

*Updated 1 November 2026*