Tutorial paper compares multivariate generalised additive models and functional principal component analysis for tongue contours, suggesting a preferred method.
This tutorial paper introduces two approaches to modelling tongue conoutr data obtained with DeepLabCut using Multivariate Generalised Additive Models (MGAMs) and Multivariate Functional Principal Component Analysis (MFPCA). For each method, we present a fully commented analysis of two illustrative data sets: VC coarticulation in Italian and Polish, and consonant emphaticness in Lebanese Arabic. All the materials (inlcuding data and code) are available in the research compendium of the tutorial at . We conclude by discussing advantages and disadvantages of the two methods (MGAM and MFPCA) and we recommend researchers to prefer MFPCA over MGAM as an initial step for modelling tongue contours.
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Coretta et al. (2025) studied this question.
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