An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

Real-time magnetic resonance imaging (rtMRI) is a technique that provides high contrast
videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful
method for capturing the dynamics of speech and other vocal behaviours by imaging
structures internal to the mouth and throat. These images provide a means of studying the
physiological basis for speech, singing, expressions of emotion, and swallowing that are
otherwise not accessible for external observation. However, taking quantitative
measurements from these images is notoriously difficult. We introduce a signal processing
pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification
of the dynamic morphology of the vocal tract. Our approach performs simple tissue
classification, but constrained to a researcher-specified region of interest. This combination
facilitatesfeature extraction while retaining the domain specific expertise of a human analyst.
We demonstrate that this pipeline generalises well across datasets covering behaviours such
as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable
outcomes across analysts, particularly among users with domain specific expertise. With this
article, we make this pipeline available for immediate use by the research community, and
further suggest that it may contribute to the continued development of fully automated
methods based on deep learning algorithms.