HCDec 19, 2016
A real-time framework for visual feedback of articulatory data using statistical shape models
arXiv:1612.06114v13 citationsHas Code
Originality Synthesis-oriented
AI Analysis
This provides a tool for researchers in speech science and linguistics to visualize articulatory data, but it is incremental as it builds on existing statistical shape modeling techniques.
The authors tackled the problem of visualizing electromagnetic articulography data in real-time by developing an open-source framework with anatomically accurate models derived from multilinear subspace learning.
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.