Acoustic-to-articulatory inversion based on local regression

This paper presents an Acoustic-to-Articulatory inversion method based on local regression. Two types of local regression, a non-parametric and a local linear regression have been applied on a corpus containing simultaneous recordings of positions of articulators and the corresponding acoustics. A maximum likelihood trajectory smoothing using the estimated dynamics of the articulators is also applied on the regression estimates. The average root mean square error in estimating articulatory positions, given the acoustics, is 1.56 mm for the nonparametric regression and 1.52 mm for the local linear regression. The local linear regression is found to perform significantly better than regression using Gaussian Mixture Models using the same acoustic and articulatory features.