Bayesian multivariate linear mixed effects models for speech research: a tutorial using brms

Purpose: When studying how factors influence multiple outcomes (e.g., acoustic measures in phonetics), researchers often analyze each outcome separately using a univariate approach. However, this approach ignores relationships between outcomes, which can reduce estimate accuracy and make it difficult to examine how effects are related across outcomes. A multivariate approach addresses these issues by modelling all outcomes jointly. This tutorial illustrates how to fit Bayesian multivariate linear mixed effects models using the R package brms. Method: We present three example applications in phonetic research, using a corpus of Greek utterances. We focus on a rising accent, that is, a deliberate f_0 movement temporally aligned with a word’s stressed syllable and used to highlight that word in speech. Specifically, we examine whether the phonetic characteristics of the rising accent depend on two factors: (a) the presence of a preceding accent within the same utterance, and (b) the location of the stress in the accented word relative to its final syllable. Results: The multivariate approach reduced uncertainty in population-level effect estimates compared to univariate models and provided a convenient way to examine correlations among effects across outcomes. Conclusions: This tutorial provides guidance on implementing Bayesian multivariate linear mixed effects models and demonstrates their potential.