Statistics in Phonetics

TL;DR

This paper introduces statistical and signal processing methods used in phonetics, highlighting recent shifts towards advanced modeling techniques driven by large speech datasets and machine learning applications.

Contribution

It provides a comprehensive overview of statistical methods in phonetics, emphasizing recent advances like Bayesian modeling and curve data analysis.

Findings

Shift from fixed to random effects models

Use of Bayesian and curve data methods

Impact of machine learning on phonetic analysis

Abstract

Phonetics is the scientific field concerned with the study of how speech is produced, heard and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this paper, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics, speech inversion, sound change, and speech technology), an overview of the statistical methods for analyzing their data, and an introduction to the signal processing methods commonly applied to speech recordings. A major transition in the statistical modeling of phonetic data has been the shift from fixed effects to random effects regression models, the modeling of curve data (for instance via GAMMs or FDA methods), and the use of Bayesian methods. This shift has been driven in part by the increased focus on large speech corpora in phonetics, which has been driven by machine learning methods such as forced alignment. We conclude by identifying opportunities for future research.