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

Social Capital Online

"What is ‘social capital’? The enormous positivity surrounding it conceals the instrumental economic rationality underpinning the notion as corporations silently sell consumer data for profit. Status chasing is just one aspect of a process of transforming qualitative aspects of social interactions into quantifiable metrics for easier processing, prediction, and behavioural shaping. A work of critical media studies, Social Capital Online examines the idea within the new ‘network spectacle’ of digital capitalism via the ideas of Marx, Veblen, Debord, Baudrillard and Deleuze. Explaining how such phenomena as online narcissism and aggression arise, Faucher offers a new theoretical understanding of how the spectacularisation of online activity perfectly aligns with the value system of neoliberalism and its data worship. Even so, at the centre of all, lie familiar ideas – alienation and accumulation – new conceptions of which he argues are vital for understanding today’s digital society.

Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes

This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980

The effects of English proficiency on the processing of Bulgarian-accented English by Bulgarian-English bilinguals

This dissertation explores the potential benefit of listening to and with one’s first-language accent, as suggested by the Interspeech Intelligibility Benefit Hypothesis (ISIB). Previous studies have not consistently supported this hypothesis. According to major second language learning theories, the listener’s second language proficiency determines the extent to which the listener relies on their first language phonetics. Hence, this thesis provides a novel approach by focusing on the role of English proficiency in the understanding of Bulgarian-accented English for Bulgarian-English bilinguals. The first experiment investigated whether evoking the listeners’ L1 Bulgarian phonetics would improve the speed of processing Bulgarian-accented English words, compared to Standard British English words, and vice versa. Listeners with lower English proficiency processed Bulgarian-accented English faster than SBE, while high proficiency listeners tended to have an advantage with SBE over Bulgarian accent. The second experiment measured the accuracy and reaction times (RT) in a lexical decision task with single-word stimuli produced by two L1 English speakers and two Bulgarian-English bilinguals. Listeners with high proficiency in English responded slower and less accurately to Bulgarian-accented speech compared to L1 English speech and compared to lower proficiency listeners. These accent preferences were also supported by the listener’s RT adaptation across the first experimental block. A follow-up investigation compared the results of L1 UK English listeners to the bilingual listeners with the highest proficiency in English. The L1 English listeners and the bilinguals processed both accents with similar speed, accuracy and adaptation patterns, showing no advantage or disadvantage for the bilinguals. These studies support existing models of second language phonetics. Higher proficiency in L2 is associated with lesser reliance on L1 phonetics during speech processing. In addition, the listeners with the highest English proficiency had no advantage when understanding Bulgarian-accented English compared to L1 English listeners, contrary to ISIB. Keywords: Bulgarian-English bilinguals, bilingual speech processing, L2 phonetic development, lexical decision, proficienc