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Exploring British accents: Modelling the trap–bath split with functional data analysis

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  • Aranya Koshy
  • Shahin Tavakoli

Abstract

The sound of our speech is influenced by the places we come from. Great Britain contains a wide variety of distinctive accents which are of interest to linguistics. In particular, the ‘a’ vowel in words like ‘class’ is pronounced differently in the North and the South. Speech recordings of this vowel can be represented as formant curves or as mel‐frequency cepstral coefficient curves. Functional data analysis and generalised additive models offer techniques to model the variation in these curves. Our first aim was to model the difference between typical Northern and Southern vowels /æ/ and /ɑ/, by training two classifiers on the North‐South Class Vowels dataset collected for this paper. Our second aim is to visualise geographical variation of accents in Great Britain. For this we use speech recordings from a second dataset, the British National Corpus (BNC) audio edition. The trained models are used to predict the accent of speakers in the BNC, and then we model the geographical patterns in these predictions using a soap film smoother. This work demonstrates a flexible and interpretable approach to modelling phonetic accent variation in speech recordings.

Suggested Citation

  • Aranya Koshy & Shahin Tavakoli, 2022. "Exploring British accents: Modelling the trap–bath split with functional data analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 773-805, August.
    • Handle: RePEc:bla:jorssc:v:71:y:2022:i:4:p:773-805
    DOI: 10.1111/rssc.12555
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    References listed on IDEAS

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    1. Davide Pigoli & Pantelis Z. Hadjipantelis & John S. Coleman & John A. D. Aston, 2018. "The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1103-1145, November.
    2. Shahin Tavakoli & Davide Pigoli & John A. D. Aston & John S. Coleman, 2019. "A Spatial Modeling Approach for Linguistic Object Data: Analyzing Dialect Sound Variations Across Great Britain," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1081-1096, July.
    3. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    4. Clara Happ & Sonja Greven, 2018. "Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 649-659, April.
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