Vocal tract shape trajectory estimation using MLP analysis-by-synthesis

The objective of this work is a computationally efficient method for inferring vocal tract shape trajectories from acoustic speech signals. We use an multilayer perceptron (MLP) to model the vocal tract shape-to-acoustics mapping, then in an analysis-by-synthesis approach, optimise an objective function that includes both the accuracy of the spectrum approximation and the credibility of the vocal tract dynamics. This optimisation carries out gradient descent using backpropagation of derivatives through the MLP. Employing a series of MLPs of increasing order avoids getting trapped in local optima caused by the many-to-one mapping between vocal tract shapes and acoustics. We obtain two orders of magnitude speed increase compared with our previous methods using codebooks and direct optimisation of a synthesiser