Normalisation articulatoire du locuteur par méthodes de décomposition tri-linéaire basées sur des données IRM (Articulatory speaker normalisation based on MRI-data using three-way linear decomposition methods) [in French]

Articulatory speaker normalisation based on MRI-data using three-way linear decomposition methods The aim of this study was to characterise, to model and to compare the different lingual articulatory strategies of a group of speakers. Individual principal component analysis (PCA) models and multi-linear decomposition methods have been applied to the tongue contours extracted from a magnetic resonance imaging (MRI) corpus of seven speakers articulating 63 French vowels and consonants. On the average over the seven speakers, using 4 components, the Root Mean Square prediction Error (RMSE) was 0.13 cm for the individual PCA models while the RMSE for the parallel factor model (PARAFAC) was 0.29 cm, accounting for a percentage of variance explanation of 91% and 62%, respectively. A multi-linear regression (MRL) model could predict, with 10 components, the tongue contour of a target subject from a given source subject, with about 65% of the variance explained and an RMSE of 0.38 cm. All the models have been assessed by a leave-one-out cross-validation procedure.