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Bayesian Multivariate Linear Mixed-Effects Models for Speech Research: A Tutorial Using brms
Na Hu1, Paul-Christian Bürkner2, Amalia Arvaniti1
1Department of Modern Languages and Cultures, Radboud University, Nijmegen, the Netherlands.
Summary
Bayesian multivariate linear mixed-effects models offer a powerful approach for analyzing multiple outcomes in phonetics, reducing uncertainty and revealing relationships between effects across different acoustic measures.
Area of Science:
- Linguistics
- Statistics
- Computational Phonetics
Background:
- Univariate analyses of multiple outcomes in phonetics can ignore inter-outcome relationships, reducing accuracy.
- Multivariate approaches model outcomes jointly, improving estimate accuracy and examining cross-outcome effects.
Purpose of the Study:
- To illustrate fitting Bayesian multivariate linear mixed-effects models.
- To demonstrate the utility of these models in phonetic research using the R package brms.
Main Methods:
- Application of Bayesian multivariate linear mixed-effects models.
- Analysis of Greek utterances focusing on rising accents and their phonetic characteristics.
- Investigation of factors influencing rising accents: preceding accent and stress location.
Main Results:
- The multivariate approach reduced uncertainty in population-level effect estimates compared to univariate models.
- The models provided a convenient method for examining correlations among effects across different acoustic outcomes.
Conclusions:
- Bayesian multivariate linear mixed-effects models offer a robust framework for phonetic research.
- This tutorial guides implementation and highlights the potential of these advanced statistical techniques.
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