(Commercial) Automatic Speech Recognition as a Tool in Sociolinguistic Research

As speech datasets used in sociolinguistic research increase in size, laborious and time-intensive manual orthographic transcription is a challenge, limiting the amount of (transcribed) data which can be analysed. In this paper, I discuss the use of (commercial) automatic speech recognition (ASR) as a tool in sociolinguistic research in the context of a case study: the Lothian Diary Project. I describe the kinds of errors produced by two commercial ASR systems for British English within the broader context of algorithmic bias in ASR, and suggest some best practices when working with ASR in sociolinguistic work

Language variation, automatic speech recognition and algorithmic bias

In this thesis, I situate the impacts of automatic speech recognition systems in relation to sociolinguistic theory (in particular drawing on concepts of language variation, language ideology and language policy) and contemporary debates in AI ethics (especially regarding algorithmic bias and fairness). In recent years, automatic speech recognition systems, alongside other language technologies, have been adopted by a growing number of users and have been embedded in an increasing number of algorithmic systems. This expansion into new application domains and language varieties can be understood as an expansion into new sociolinguistic contexts. In this thesis, I am interested in how automatic speech recognition tools interact with this sociolinguistic context, and how they affect speakers, speech communities and their language varieties. Focussing on commercial automatic speech recognition systems for British Englishes, I first explore the extent and consequences of performance differences of these systems for different user groups depending on their linguistic background. When situating this predictive bias within the wider sociolinguistic context, it becomes apparent that these systems reproduce and potentially entrench existing linguistic discrimination and could therefore cause direct and indirect harms to already marginalised speaker groups. To understand the benefits and potentials of automatic transcription tools, I highlight two case studies: transcribing sociolinguistic data in English and transcribing personal voice messages in isiXhosa. The central role of the sociolinguistic context in developing these tools is emphasised in this comparison. Design choices, such as the choice of training data, are particularly consequential because they interact with existing processes of language standardisation. To understand the impacts of these choices, and the role of the developers making them better, I draw on theory from language policy research and critical data studies. These conceptual frameworks are intended to help practitioners and researchers in anticipating and mitigating predictive bias and other potential harms of speech technologies. Beyond looking at individual choices, I also investigate the discourses about language variation and linguistic diversity deployed in the context of language technologies. These discourses put forward by researchers, developers and commercial providers not only have a direct effect on the wider sociolinguistic context, but they also highlight how this context (e.g., existing beliefs about language(s)) affects technology development. Finally, I explore ways of building better automatic speech recognition tools, focussing in particular on well-documented, naturalistic and diverse benchmark datasets. However, inclusive datasets are not necessarily a panacea, as they still raise important questions about the nature of linguistic data and language variation (especially in relation to identity), and may not mitigate or prevent all potential harms of automatic speech recognition systems as embedded in larger algorithmic systems and sociolinguistic contexts

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

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 (https://groups.inf.ed.ac.uk/edacc/) under CC-BY-SA license.Comment: Accepted to IEEE ICASSP 202