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This paper aims to introduce and compare two statistical approaches for analyzing tongue contour data from ultrasound images.

March 2, 2026Open Access

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Multivariate Analyses of Tongue Contours from Ultrasound Tongue Imaging

Key Points

This paper aims to introduce and compare two statistical approaches for analyzing tongue contour data from ultrasound images.
Uses multivariate generalized additive models (MGAMs) and multivariate functional principal component analysis (MFPCA).
Analyzes data sets related to coarticulation in Italian and Polish and consonant emphaticness in Lebanese Arabic.
Provides a complete code and data resources for the analyses.
Identifies the advantages of MFPCA over MGAM for modeling tongue contours.
Demonstrates how each method applies to different linguistic contexts.

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/mvᵤti. 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.

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Cite This Study

Coretta et al. (Sat,) studied this question.

synapsesocial.com/papers/69a52e34f1e85e5c73bf1b78 https://doi.org/https://doi.org/10.1177/00238309261419120

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