Normalization of articulatory data through Procrustes transformations and analysis-by-synthesis

We describe and compare three methods that can be used to normalize articulatory data across speakers. The methods seek to explain systematic anatomical differences between a source and target speaker without modifying the articulatory velocities of the source speaker. The first method is the classical Procrustes transform, which allows for a global translation, rotation, and scaling of articulator positions. We present an extension to the Procrustes transform that allows independent translations of each articulator. The additional parameters provide a 35% increase in articulatory similarity between pairs of speakers when compared to classical Procrustes. The proposed extension is finally coupled with a data-driven articulatory synthesizer in an analysis-by-synthesis loop to select model parameters that best explain the predicted acoustic (rather than articulatory) differences. This normalization method is able to increase acoustic similarity between source and the target speaker by 34%. However, it also reduces articulatory similarity by 22%, which suggest that improvements in acoustic similarity do not necessarily require an increase in articulatory similarity.