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The statistical analysis of acoustic phonetic data: exploring differences between spoken Romance languages

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  • Davide Pigoli
  • Pantelis Z. Hadjipantelis
  • John S. Coleman
  • John A. D. Aston

Abstract

The historical and geographical spread from older to more modern languages has long been studied by examining textual changes and in terms of changes in phonetic transcriptions. However, it is more difficult to analyse language change from an acoustic point of view, although this is usually the dominant mode of transmission. We propose a novel analysis approach for acoustic phonetic data, where the aim will be to model the acoustic properties of spoken words statistically. We explore phonetic variation and change by using a time–frequency representation, namely the log‐spectrograms of speech recordings. We identify time and frequency covariance functions as a feature of the language; in contrast, mean spectrograms depend mostly on the particular word that has been uttered. We build models for the mean and covariances (taking into account the restrictions placed on the statistical analysis of such objects) and use these to define a phonetic transformation that models how an individual speaker would sound in a different language, allowing the exploration of phonetic differences between languages. Finally, we map back these transformations to the domain of sound recordings, enabling us to listen to the output of the statistical analysis. The approach proposed is demonstrated by using recordings of the words corresponding to the numbers from 1 to 10 as pronounced by speakers from five different Romance languages.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:67:y:2018:i:5:p:1103-1145
    DOI: 10.1111/rssc.12258
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    as
    1. Mark Fiecas & Hernando Ombao, 2016. "Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1440-1453, October.
    2. Davide Pigoli & John A. D. Aston & Ian L. Dryden & Piercesare Secchi, 2014. "Distances and inference for covariance operators," Biometrika, Biometrika Trust, vol. 101(2), pages 409-422.
    3. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    4. Gerda Claeskens & Mia Hubert & Leen Slaets & Kaveh Vakili, 2014. "Multivariate Functional Halfspace Depth," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 411-423, March.
    5. Garcia, Damien, 2010. "Robust smoothing of gridded data in one and higher dimensions with missing values," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1167-1178, April.
    6. Wensheng Guo, 2002. "Functional Mixed Effects Models," Biometrics, The International Biometric Society, vol. 58(1), pages 121-128, March.
    7. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    8. Scott A. Bruce & Martica H. Hall & Daniel J. Buysse & Robert T. Krafty, 2018. "Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series," Biometrics, The International Biometric Society, vol. 74(1), pages 260-269, March.
    9. Antonio Cuevas & Manuel Febrero & Ricardo Fraiman, 2007. "Robust estimation and classification for functional data via projection-based depth notions," Computational Statistics, Springer, vol. 22(3), pages 481-496, September.
    10. Geoff K. Nicholls & Russell D. Gray, 2008. "Dated ancestral trees from binary trait data and their application to the diversification of languages," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 545-566, July.
    11. P. Constantinou & P. Kokoszka & M. Reimherr, 2017. "Testing separability of space-time functional processes," Biometrika, Biometrika Trust, vol. 104(2), pages 425-437.
    12. Robert T. Krafty & Martica Hall & Wensheng Guo, 2011. "Functional mixed effects spectral analysis," Biometrika, Biometrika Trust, vol. 98(3), pages 583-598.
    13. Victor Ginsburgh & Shlomo Weber, 2011. "How Many Languages Do We Need? The Economics of Linguistic Diversity," Economics Books, Princeton University Press, edition 1, number 9481.
    14. Feng, Qing & Jiang, Meilei & Hannig, Jan & Marron, J.S., 2018. "Angle-based joint and individual variation explained," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 241-265.
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    3. Soham Sarkar & Victor M. Panaretos, 2022. "CovNet: Covariance networks for functional data on multidimensional domains," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1785-1820, November.
    4. Anthony Ebert & Kerrie Mengersen & Fabrizio Ruggeri & Paul Wu, 2021. "Curve Registration of Functional Data for Approximate Bayesian Computation," Stats, MDPI, vol. 4(3), pages 1-14, September.
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