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Article published In: Review of Cognitive Linguistics
Vol. 16:2 (2018) ► pp.494–518

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English as a lingua franca in Europe: The identification of L1 and L2 accents

A multifactorial analysis of pan-European experimental data

 | Universidad Complutense de Madrid
 | University of Leuven
 | University of Leuven
Published online: 5 November 2018
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https://doi.org/10.1075/rcl.00019.kri
Abstract
While empirical research on attitudes towards languages and linguistic varieties has become increasingly popular from the 1960s onwards (e.g. Lambert, W. E., Hodgson, R. C., Gardner, R. C., & Fillenbaum, S. (1960). Evaluational reactions to spoken language. Journal of Abnormal and Social Psychology, 601, 44–51. Google Scholar logo with link to Google Scholar), experimental investigations into the ability to correctly identify the origin of speakers are in comparison still relatively scarce. We know that the ability to correlate a stretch of uncategorised speech (token) with a series of models (types) is experientially acquired in early childhood (e.g. (2010). Lectal acquisition and linguistic stereotype formation: An empirical study. In D. Geeraerts, G. Kristiansen, & Y. Peirsman (Eds.) Advances in cognitive sociolinguistics. Berlin/New York: Mouton de Gruyter. Google Scholar logo with link to Google Scholar), but how similar are those abilities in adulthood and across European nations? English as a Lingua Franca (ELF) has become an integral part of the linguistic reality in Europe (and of the linguistic scenario in the entire world) (e.g. Jenkins, J., Baker, W., & Dewey, M. (2018). The Routledge handbook of English as a Lingua Franca. London: Routledge.Google Scholar logo with link to Google Scholar). Whenever we communicate with anyone who is not a speaker of our own native language in any European country, most of the time we communicate in English. But does our L1 accent still shine through? Will we be recognised (and in most cases probably also stereotypically judged) on the basis of just a short stretch of speech when we communicate in ELF? In Part I of this paper we outline the design of the first large-scale pan-European project on L1 and L2 identifications of ELF in Europe, including 785 respondents from 8 countries. Exploratory analyses confirm the hypothesis that statistically significant asymmetries would show up across different European countries or regions. In Part II of this paper we then aim to explain these asymmetries through a multifactorial statistical analysis (Geeraerts, D., Grondelaers, S., & Speelman, D. (1999). Convergentie en divergentie in de Nederlandse woordenschat. Amsterdam: Meertens Instituut.Google Scholar logo with link to Google Scholar; Tagliamonte, S., & Baayen, H. R. (2012). Models, forests, and trees of York English: Was/were variation as a case study for statistical practice. Language Variation and Change, 24(2), 135–178. Google Scholar logo with link to Google Scholar; Speelman, D., Heylen, K., & Geeraerts, D. (2018). Mixed-effects regression models in linguistics. New York: Springer. Google Scholar logo with link to Google Scholar).
Article outline
  • 1.Introduction
  • 2.Theoretical background: Lectal varieties as socially diagnostic triggers of meaning
  • 3.Identification experiment: Design and exploration
    • 3.1Design
    • 3.2Exploration
  • 4.Multifactorial analysis: Design
    • 4.1Response variable
    • 4.2Predictor variables
    • 4.3Analytic procedure
  • 5.Multifactorial analysis: Results
    • 5.1Mixed effect regression model
    • 5.2Conditional inference tree and random forest
  • 6.Conclusion
  • Notes
  • References
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