Dr Nina Markl

Research Fellow (IADS)
Department of Language and Linguistics
Dr Nina Markl



I am a researcher working at the intersection of sociolinguistics, speech technologies and AI ethics. I am particularly interested in language variation and change and the impact of speech technologies on speech communities. More broadly, I am interested in understanding the socio-technical contexts and impacts of computing technologies, in particular machine learning and "artificial intelligence". I am a (socio)linguist and (somewhat skeptical) computer scientist by training, but have a strong interest in science and technology studies, sociology and political studies. In my work I try to bring different fields, and different researchers, into conversation. Within and outwith academia, I'm committed to collaboration to foster sustainable, anti-racist and supportive learning, working and living environments. I am a Research Fellow at the Institute for Analytics and Data Science and the Department for Language and Linguistics at the University of Essex. Before moving to England, I was a student and (part-time) tutor and research assistant at the University of Edinburgh in Scotland. My PhD research was supervised by Dr Catherine Lai and Prof Lauren Hall-Lew at the UKRI CDT for NLP at the University of Edinburgh.


  • PhD University of Edinburgh,

  • MA (Hons) Linguistics University of Edinburgh,


University of Essex

  • Research Fellow, Institute for Analytics and Data Science & Department for Language and Linguistics, University of Essex (21/8/2023 - present)

Teaching and supervision

Current teaching responsibilities

  • Forensic Linguistics (LG364)


Publications (1)

Markl, N. and McNulty, SJ., (2022). Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR

Journal articles (4)

Cowie, C., Hall-Lew, L., Elliott, Z., Klingler, A., Markl, N. and McNulty, SJ., Imagining the city in lockdown: Place in the COVID-19 self-recordings of the Lothian Diary Project. Frontiers in Artificial Intelligence. 5

Markl, N., (2023). “I can't see myself ever living any[w]ere else”: Variation in (HW) in Edinburgh English. Language Variation and Change. 35 (1), 79-105

Hall-Lew, L., Cowie, C., Lai, C., Markl, N., McNulty, SJ., Liu, S-JS., Llewellyn, C., Alex, B., Elliott, Z. and Klingler, A., (2022). The Lothian Diary Project: sociolinguistic methods during the COVID-19 lockdown. Linguistics Vanguard. 8 (s3), 321-330

Hall-Lew, L., Cowie, C., McNulty, SJ., Markl, N., Liu, S-JS., Lai, C., Llewellyn, C., Alex, B., Fang, N., Elliott, Z. and Klingler, A., (2021). The Lothian Diary Project: Investigating the Impact of the COVID-19 Pandemic on Edinburgh and Lothian Residents. Journal of Open Humanities Data. 7

Conferences (10)

Markl, N. and Lai, C., Everyone has an accent

Markl, N., Wallington, E., Klejch, O., Reitmaier, T., Bailey, G., Pearson, J., Jones, M., Robinson, S. and Bell, P., Automatic Transcription and (De)Standardisation

Markl, N., Hall-Lew, L. and Lai, C., Language Technologies as if People Mattered: Centering Communities in Language Technology Development

Reitmaier, T., Wallington, E., Klejch, O., Markl, N., Lam-Yee-Mui, L-M., Pearson, J., Jones, M., Bell, P. and Robinson, S., (2023). Situating Automatic Speech Recognition Development within Communities of Under-heard Language Speakers

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

Markl, N., (2022). Language variation and algorithmic bias: understanding algorithmic bias in British English automatic speech recognition

Markl, N. and McNulty, SJ., (2022). Language technology practitioners as language managers: arbitrating data bias and predictive bias in ASR

Markl, N., (2022). Mind the data gap(s): Investigating power in speech and language datasets

Markl, N. and Lai, C., (2021). Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation

Markl, N., Burchell, L., Hosking, T., Webber, B. and Chi, J., (2020). Querent Intent in Multi-Sentence Questions



Colchester Campus