Gaussian tree constraints applied to acoustic linguistic functional data

Nathaniel Shiers, John A.D. Aston, Jim Q. Smith and John S. Coleman

Journal of Multivariate Analysis, 2017, vol. 154, issue C, 199-215

Abstract: Evolutionary models of languages are usually considered to take the form of trees. With the development of so-called tree constraints the plausibility of the tree model assumptions can be assessed by checking whether the moments of observed variables lie within regions consistent with Gaussian latent tree models. In our linguistic application, the data set comprises acoustic samples (audio recordings) from speakers of five Romance languages or dialects. The aim is to assess these functional data for compatibility with a hereditary tree model at the language level. A novel combination of canonical function analysis (CFA) with a separable covariance structure produces a representative basis for the data. The separable-CFA basis is formed of components which emphasize language differences whilst maintaining the integrity of the observational language-groupings. A previously unexploited Gaussian tree constraint is then applied to component-by-component projections of the data to investigate adherence to an evolutionary tree. The results highlight some aspects of Romance language speech that appear compatible with an evolutionary tree model but indicate that it would be inappropriate to model all features as such.

Keywords: Gaussian tree constraints; Canonical variate analysis; Functional data analysis; Phonetics; Separable covariance (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.jmva.2016.09.015

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