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

2023

Situating Automatic Speech Recognition Development within Communities of Under-heard Language Speakers

Thomas Reitmaier, Electra Wallington, Ondřej Klejch, Nina Markl, and 5 more authors

In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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In this paper we develop approaches to automatic speech recognition (ASR) development that suit the needs and functions of under-heard language speakers. Our novel contribution to HCI is to show how community-engagement can surface key technical and social issues and opportunities for more effective speech-based systems. We introduce a bespoke toolkit of technologies and showcase how we utilised the toolkit to engage communities of under-heard language speakers; and, through that engagement process, situate key aspects of ASR development in community contexts. The toolkit consists of (1) an information appliance to facilitate spoken-data collection on topics of community interest, (2) a mobile app to create crowdsourced transcripts of collected data, and (3) demonstrator systems to showcase ASR capabilities and to feed back research results to community members. Drawing on the sensibilities we cultivated through this research, we present a series of challenges to the orthodoxy of state-of-the-art approaches to ASR development.
"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.