Model-based Synthesis and Transformation of Voiced Sounds

In this work a glottal model loosely based on the Ishizaka and Flanagan model is proposed, where the number of parameters is drastically reduced. First, the glottal excitation waveform is estimated, together with the vocal tract filter parameters, using inverse filtering techniques. Then the estimated waveform is used in order to identify the nonlinear glottal model, represented by a closedloop configuration of two blocks: a second order resonant filter, tuned with respect to the signal pitch, and a regressor-based functional, whose coefficients are estimated via nonlinear identification techniques. The results show that an accurate identification of real data can be achieved with less than 10 regressors of the nonlinear functional, and that an intuitive control of fundamental features, such as pitch and intensity, is allowed by acting on the physically informed parameters of the model.