Generalised additive mixed models for dynamic analysis in linguistics: a practical introduction

TL;DR

This paper provides a practical introduction to Generalised Additive Mixed Models (GAMMs) for dynamic speech analysis in linguistics, explaining core concepts and guiding users through implementation and significance testing in R.

Contribution

It offers a comprehensive, accessible tutorial on applying GAMMs to linguistic data, including model fitting, evaluation, and significance testing, tailored for practical use.

Findings

Demonstrates how to fit GAMMs to speech data

Explains significance testing procedures for GAMMs

Provides example analyses in R

Abstract

This is a hands-on introduction to Generalised Additive Mixed Models (GAMMs) in the context of linguistics with a particular focus on dynamic speech analysis (e.g. formant contours, pitch tracks, diachronic change, etc.). The main goal is to explain some of the main ideas underlying GAMMs, and to provide a practical guide to frequentist significance testing using these models. The introduction covers a range of topics including basis functions, the smoothing penalty, random smooths, difference smooths, smooth interactions, model comparison and autocorrelation. It is divided into two parts. The first part looks at what GAMMs are, how they work and why/when we should use them. Although the reader can replicate some of the example analyses in this section, this is not essential. The second part is a tutorial introduction that illustrates the process of fitting and evaluating GAMMs in the R statistical software environment, and the reader is strongly encouraged to work through the examples on their own machine.