The sociolinguistic landscape of Fryslân offers an opportunity to examine how language dominance (hereafter LD) and language identity (hereafter LI; Joseph, 2006) shape cognition, social evaluation, and communication across human and A.I. contexts. Despite extensive work on language processing and code-switching, no research has investigated how Frisian–Dutch LD and LI influence production, perception, and interaction, and how this connects to digital language vitality. This PhD project addresses this gap through a three-part, human-centric investigation of how LD and LI operate across a spectrum of bilingualism – from cognition to perception to human-machine interaction:
● Study 1: Switche – examines how LD and LI shape cognition, testing Frisian-Dutch speakers in a language switching picture naming task (PNT) with cognate and non-cognate words (cf. Kirk et al., 2022).
● Study 2: Harkje – investigates how LD and LI shape perception. Frisian speakers will evaluate stimuli – human baseline and matched synthetic Frisian and Dutch voices – using sociolinguistic measures including authenticity, comprehensibility, sociability, trustworthiness, and competence (Hendriks et al., 2023). Harkje assesses how LD and LI influence speakers’ perceptions and their willingness to use Frisian-language A.I. tools.
● Study 3: Prate – explores how LD and LI shape real-time interaction, communicative accommodation, and trust (cf. Bailey et al., 2022; Dong & Zhou, 2023) across human-robot interaction (HRI) conditions. These include a distinctly “Frisian” robot (e.g., one that produces local speech/dialectal patterns), a monolingual Dutch robot, and a Frisian–Dutch code-switching robot.
Together, these studies seek to establish how LD and LI function as complementary yet distinct cognitive and social filters that modulate how speakers activate languages, evaluate voices, and engage with interlocutors – whether human or artificial. Ultimately, understanding these mechanisms is essential in developing A.I. and language technologies that resonate with a diverse array of speakers, providing a framework to enhance language vitality in the digital era.
Sources:
Bailey, D. E., Faraj, S., Hinds, P. J., Leonardi, P. M., & von Krogh, G. (2022). We are all theorists of technology now: A relational perspective on emerging technology and organizing. Organization Science, 33(1), 1-18. https://doi.org/10.1287/orsc.2021.1562
Dong, Y., & Zhou, X. (2023). Advancements in AI-driven multilingual comprehension for social robot interactions: An extensive review. Electronic Research Archive, 31(11), 6600–6633. https://doi.org/10.3934/era.2023334
Hendriks, B., van Meurs, F., & Usmany, N. (2023). The effects of lecturers’ non-native accent strength in English on intelligibility and attitudinal evaluations by native and non-native English students. Language Teaching Research, 27(6), 1378–1407. https://doi.org/10.1177/1362168820983145
Joseph, J. E. (2006). Identity and language. In K. Brown (Ed.), Encyclopedia of Language & Linguistics (2nd ed., pp. 486–492). Elsevier. https://doi.org/10.1016/B0-08-044854-2/01283-9
Kirk, N. W., Declerck, M., Kemp, R. J., & Kempe, V. (2022). Language control in regional dialect speakers – monolingual by name, bilingual by nature? Bilingualism: Language and Cognition, 25(3), 511–520. https://doi.org/10.1017/S1366728921000973