Real-time magnetic resonance imaging is a technique that provides high contrast videographic data of the vocal tract that allow researchers to observe the internal structures that shape the sounds of speech. However, structural features need to be extracted from these vocal tract images to make them useful to researchers. We have developed a semi-automated processing pipeline that produces outlines of the vocal tract to quantify vocal tract morphology. Our approach uses simple tissue classification constrained to pixels that analysts have identified as likely to contain the vocal tract and surrounding tissue. This approach is supplemented with multiple opportunities for the analyst to intervene in order to ensure that outputs are robust to errors. Although this approach is more labour intensive than more fully automated alternatives, these costs are offset by the benefits of improving the quality of measurements. We demonstrate that this pipeline can be generalised to a range of datasets and that it remains reliable across analysts, particularly among analysts with vocal tract expertise. The pipeline’s reliance on user input presents a challenge to scalability if applied to very large. Measurements produced by this pipeline could be provide a broader scope of training data for fully automated methods in an effort to improve their generalisability.
Dark tone quality and vocal tract shaping in soprano song production: Insights from real-time MRI
