AURORA Model of Formant-to-Tongue Inversion for Didactic and Clinical Applications
This work addresses a specific problem for students, linguists, and speech therapy practitioners by offering an incremental tool for formant-to-tongue inversion.
The paper tackles the problem of predicting tongue displacement and shape from vowel formants by developing the AURORA model, which uses ultrasound and acoustic data from 40 English speakers, and provides tools like a Shiny app for didactic and clinical applications.
This paper outlines the conceptual and computational foundations of the AURORA (Acoustic Understanding and Real-time Observation of Resonant Articulations) model. AURORA predicts tongue displacement and shape in vowel sounds based on the first two formant values. It is intended as a didactic aid helping to explain the relationship between formants and the underlying articulation, as well as a foundation for biofeedback applications. The model is informed by ultrasound tongue imaging and acoustic data from 40 native speakers of English. In this paper we discuss the motivation for the model, the modelling objectives as well as the model architecture. We provide a qualitative evaluation of the model, focusing on selected tongue features. We then present two tools developed to make the model more accessible to a wider audience, a Shiny app and a prototype software for real-time tongue biofeedback. Potential users include students of phonetics, linguists in fields adjacent to phonetics, as well as speech and language therapy practitioners and clients.