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 work as a Research Fellow at the Institute for Analytics and Data Science and the Department of Language and Linguistics at the University of Essex in Colchester, in south-east England.

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.

selected publications

2024

Language Technologies as If People Mattered: Centering Communities in Language Technology Development

Nina Markl, Lauren Hall-Lew, and Catherine Lai

In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

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In this position paper we argue that researchers interested in language and/or language technologies should attend to challenges of linguistic and algorithmic injustice together with language communities. We put forward that this can be done by drawing together diverse scholarly and experiential insights, building strong interdisciplinary teams, and paying close attention to the wider social, cultural and historical contexts of both language communities and the technologies we aim to develop.
Cultivating Spoken Language Technologies for Unwritten Languages

Thomas Reitmaier, Dani Kalarikalayil Raju, Ondrej Klejch, Electra Wallington, and 5 more authors

In Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

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We report on community-centered, collaborative research that weaves together HCI, natural language processing, linguistic, and design insights to develop spoken language technologies for unwritten languages. Across three visits to a Banjara farming community in India, we use participatory, technical, and creative methods to engage community members, collect spoken language photo annotations, and develop an information retrieval (IR) system. Drawing on orality theory, we interrogate assumptions and biases of current speech interfaces and create a simple application that leverages our IR system to match fluidly spoken queries with recorded annotations and surface corresponding photos. In-situ evaluations show how our novel approach returns reliable results and inspired the co-creation of media retrieval use-cases that are more appropriate in oral contexts. The very low (< 4h) spoken data requirements makes our approach adaptable to other contexts where languages are unwritten or have no digital language resources available.

2023

The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR

Ramon Sanabria, Nikolay Bogoychev, Nina Markl, Andrea Carmantini, and 2 more authors

In ICASSP 2023

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English is the most widely spoken language in the world, used daily by millions of people as a first or second language in many different contexts. As a result, there are many varieties of English. Although the great many advances in English automatic speech recognition (ASR) over the past decades, results are usually reported based on test datasets which fail to represent the diversity of English as spoken today around the globe. We present the first release of The Edinburgh International Accents of English Corpus (EdAcc). This dataset attempts to better represent the wide diversity of English, encompassing almost 40 hours of dyadic video call conversations between friends. Unlike other datasets, EdAcc includes a wide range of first and second-language varieties of English and a linguistic background profile of each speaker. Results on latest public, and commercial models show that EdAcc highlights shortcomings of current English ASR models. The best performing model, trained on 680 thousand hours of transcribed data, obtains an average of 19.7% word error rate (WER) – in contrast to the 2.7% WER obtained when evaluated on US English clean read speech. Across all models, we observe a drop in performance on Indian, Jamaican, and Nigerian English speakers. Recordings, linguistic backgrounds, data statement, and evaluation scripts are released on our website under CC-BY-SA 1 license. 2 We hope that this work will encourage future research on a wider range of English varieties to create more accessible speech technologies.
"I can’t see myself ever living any[w]ere else": Variation in (HW) in Edinburgh English

Nina Markl

Language Variation and Change, 2023

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Sociolinguistic research across Scotland in recent decades has documented an erosion of the phonemic contrast between /ʍ/ (as in which) and /w/ (as in witch). Based on acoustic phonetic analysis of 1,400 realizations produced by eighteen Edinburgh women born between 1938 and 1993, I argue that in the context of Edinburgh this is best understood as a complex sociolinguistic variable (HW) encompassing (at least) six fricated and fricationless variants. Realizations vary in type and relative duration of frication, voicing, and glide quality. Bayesian statistical analysis suggests that choice and realization of variants is conditioned by speaker’s social class, style, and phonetic context. Unlike some prior work, I do not find evidence of ongoing (apparent-time) change or an effect of contact with Southern British English. Fricated variants are most prevalent in formal speech styles and in the speech of middle-class women, while working-class speakers favor fricationless variants.

2022

The Lothian Diary Project: Sociolinguistic Methods during the COVID-19 Lockdown

Lauren Hall-Lew, Claire Cowie, Catherine Lai, Nina Markl, and 6 more authors

Linguistics Vanguard, Mar 2022

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The Lothian Diary Project is an interdisciplinary effort to collect self-recorded audio or video diaries of people’s experiences of COVID-19 in and around Edinburgh, Scotland. In this paper we describe how the project emerged from a desire to support community members. The diaries have been disseminated through public events, a website, an oral history project, and engagement with policymakers. The data collection method encouraged the participation of people with disabilities, racialized individuals, immigrants, and low-proficiency English/Scots speakers, all of whom are more likely to be negatively affected by COVID-19. This is of interest to sociolinguists, given that these groups have been under-represented in previous studies of linguistic variation in Edinburgh. We detail our programme of partnering with local charities to help ensure that digitally disadvantaged groups and their caregivers are represented. Accompanying survey and demographic data means that this self-recorded speech can be used to complement existing Edinburgh speech corpora. Additional sociolinguistic goals include a narrative analysis and a stylistic analysis, to characterize how different people engage creatively with the act of creating a COVID-19 diary, especially as compared to vlogs and other video diaries.
Language Variation and Algorithmic Bias: Understanding Algorithmic Bias in British English Automatic Speech Recognition

Nina Markl

In 2022 ACM Conference on Fairness, Accountability, and Transparency

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All language is characterised by variation which language users employ to construct complex social identities and express social meaning. Like other machine learning technologies, speech and language technologies (re)produce structural oppression when they perform worse for marginalised language communities. Using knowledge and theories from sociolinguistics, I explore why commercial automatic speech recognition systems and other language technologies perform significantly worse for already marginalised populations, such as second-language speakers and speakers of stigmatised varieties of English in the British Isles. Situating language technologies within the broader scholarship around algorithmic bias, consider the allocative and representational harms they can cause even (and perhaps especially) in systems which do not exhibit predictive bias, narrowly defined as differential performance between groups. This raises the question whether addressing or “fixing” this “bias” is actually always equivalent to mitigating the harms algorithmic systems can cause, in particular to marginalised communities.