Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese

Mandarin Chinese is characterized by being a tonal language; the pitch (or F ₀) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts t...

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Published in:Journal of the American Statistical Association Vol. 110; no. 510; pp. 545 - 559
Main Authors: Hadjipantelis, P. Z., Aston, J. A. D., Müller, H. G., Evans, J. P.
Format: Journal Article
Language:English
Published: United States Taylor & Francis 03-04-2015
Taylor & Francis Group, LLC
Taylor & Francis Ltd
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Summary:Mandarin Chinese is characterized by being a tonal language; the pitch (or F ₀) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample F ₀ contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online.
Bibliography:http://dx.doi.org/10.1080/01621459.2015.1006729
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JADA’s research was supported by the Engineering and Physical Sciences Research Council (EP/K021672/2). HGM’s research was supported by NSF grants DMS-1104426 and DMS-1228369. JPE’s research was supported by National Science Council (Taiwan) grant NSC 100-2628-H-001-008-MY4.
ISSN:1537-274X
0162-1459
1537-274X
DOI:10.1080/01621459.2015.1006729