Lang Speech. 2026 Feb 28:238309261419120. doi: 10.1177/00238309261419120. Online ahead of print.
ABSTRACT
This tutorial paper introduces two approaches to modeling tongue contour data obtained with DeepLabCut using multivariate generalized 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 https://github.com/stefanocoretta/mv_uti. 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 modeling tongue contours.
PMID:41761784 | DOI:10.1177/00238309261419120
