Using a nonlinear model to synthesise natural-sounding vowels

This paper describes a nonlinear model that is able to generate vowel sounds of any required duration which also contain jitter and shimmer, and hence are more natural-sounding than the equivalent sounds generated by linear prediction techniques. The model is based on a radial basis function (RBF) neural network, with a global feedback loop. The network is trained by first placing the radial basis centres onto either a subset of the input data or a fixed hyper-lattice structure. The network weights are then found so as to minimise the mean square error between the input data (which will be a stationary vowel sound segment) and the network output. Regularisation is used when calculating the weight values, as this ensures stability when the global feedback loop is connected for synthesis