Accounting for between and within-subject variability is a relatively easy task when one uses mixed effects regression [1, 2]. Modelling speaker (and items) as random effects allows for coefficients of the main effects to be adjusted to account for this type of variability [1, 2]. Using a maximal specification approach [1] allows for an accurate estimation of the within and between-subject variation, although this heavily depends on the structure of the data and the need to assess the model’s fit to allow for a meaningful interpretation of the patterns observed. When dealing with Ultrasound Tongue Imaging (UTI) data, however, anatomical differences between speakers, for instance in tongue size, lead to important within and between subject variation that can hinder any cross-speaker generalisations, unless properly dealt with. The aim of our paper is to demonstrate how this can be achieved using two types of analyses, namely static and dynamic Principal Component Analysis (PCA), and static and dynamic Generalised Additive Mixed-effects Models (GAMMs). PCA emerges as a relatively easy, simple and robust approach to account for between-subject variability using UTI data [3] and has since been employed in various studies using UTI data on laterals [4], rhotic consonants [5] and acquisition of laterals/rhotics [6]. GAMMs on the other hand, while it has already been used on UTI data trying to account for within and between-subject variability [7, 8, 9], they are still not widely used by the community to account for between-subject and gender differences observed using UTI data.