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

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

Voice quality and coda /r/ in Glasgow English in the early 20th century

We present acoustic and auditory analyses of changes to coda /r/ and voice quality in Glasgow English in the early twentieth century. Our initial acoustic analysis suggests that /r/ was weakening across the board based on an increase in F3. However, an auditory analysis of the same data finds no significant changes. An acoustic analysis of the same speakers’ vowels reveals that the shift in F3 is not unique to /r/. It reflects a change in voice quality, which we link to velarization using Vocal Profile Analysis. We then reanalyze the acoustic /r/ data, controlling for voice quality, and find only moderate changes that are restricted to females. These findings provide new evidence for diachronic changes in voice quality, contribute to our understanding of the development of /r/ in Glasgow English, and highlight the importance of investigating speech sounds in their broader context using multiple methodologies

Changing word usage predicts changing word durations in New Zealand English

This paper investigates the emergence of lexicalized effects of word usage on word duration by looking at parallel changes in usage and duration over 130 years in New Zealand English. Previous research has found that frequent words are shorter, informative words are longer, and words in utterance-final position are also longer. It has also been argued that some of these patterns are not simply online adjustments, but are incorporated into lexical representations. While these studies tend to focus on the synchronic aspects of such patterns, our corpus shows that word-usage patterns and word durations are not static over time. Many words change in duration and also change with respect to frequency, informativity and likelihood of occurring utterance-finally. Analysis of changing word durations over this time period shows substantial patterns of co-adaptation between word usage and word durations. Words that are increasing in frequency are becoming shorter. Words that are increasing/decreasing in informativity show a change in the same direction in duration (e.g. increasing informativity is associated with increasing duration). And words that are increasingly appearing utterance-finally are lengthening. These effects exist independently of the local effects of the predictors. For example, words that are increasing utterance-finally lengthen in all positions, including utterance-medially. We show that these results are compatible with a number of different views about lexical representations, but they cannot be explained without reference to a production-perception loop that allows speakers to update their representations dynamically on the basis of their experience

A dynamic acoustic view of real-time change in word-final liquids in spontaneous Glaswegian

This paper investigates the acoustic evidence for real-time change in word-final liquids (/r/ and /l/) in a small-scale study of older male Glaswegian speakers recorded from the 1970s to the 2000s. A dynamic acoustic analysis of the first three formants across the duration of the rhyme (vowel+liquid sequence) shows significant effects of preceding and following phonetic context on the course and trajectories of the formant tracks. We also find raising of F3 for /r/ in speakers who were born and recorded more recently; F2 is lowering for /l/ in the same speakers. Comparison of F2 across the two word-final liquids suggests that /r/ is clearer than /l/ for this Scottish dialect; interestingly the polarity in resonance between /r/ and /l/ is increasing over time

nz_word_durations

Data and analysis for Sóskuthy and Hay (2017

Evaluating generalised additive mixed modelling strategies for dynamic speech analysis

Generalised additive mixed models (GAMMs) are increasingly popular in dynamic speech analysis, where the focus is on measurements with temporal or spatial structure such as formant, pitch or tongue contours. GAMMs provide a range of tools for dealing with the non-linear contour shapes and complex hierarchical organisation characteristic of such data sets. This, however, means that analysts are faced with non-trivial choices, many of which have a serious impact on the statistical validity of their analyses. This paper presents type I and type II error simulations to help researchers make informed decisions about modelling strategies when using GAMMs to analyse phonetic data. The simulations are based on two real data sets containing F2 and pitch contours, and a simulated data set modelled after the F2 data. They reflect typical scenarios in dynamic speech analysis. The main emphasis is on (i) dealing with dependencies within contours and higher-level units using random structures and other tools, and (ii) strategies for significance testing using GAMMs. The paper concludes with a small set of recommendations for fitting GAMMs, and provides advice on diagnosing issues and tailoring GAMMs to specific data sets. It is also accompanied by a GitHub repository including a tutorial on running type I error simulations for existing data sets: https://github.com/soskuthy/gamm_strategies