A real-time framework for visual feedback of articulatory data using statistical shape models

TL;DR

This paper introduces an open-source, real-time visualization framework for electromagnetic articulography data, utilizing statistical shape models for anatomically accurate tongue and palate representations.

Contribution

It presents a novel modular framework that enables real-time visualization of EMA data with anatomically precise models derived through multilinear subspace learning.

Findings

Real-time visualization of EMA data achieved.

Anatomically accurate tongue and palate models developed.

Open-source framework available for researchers.

Abstract

We present a novel open-source framework for visualizing electromagnetic articulography (EMA) data in real-time, with a modular framework and anatomically accurate tongue and palate models derived by multilinear subspace learning.