An adaptive neural control scheme for articulatory synthesis of CV sequences

• We developed a control scheme that estimates motor commands from trajectories of fleshpoints on the selected articulators.
• We used these motor commands to reproduce the trajectories of the underlying articulators in a 2nd order dynamical system.
• We implemented the control scheme using fuzzy neural networks, and tested it on consonant–vowel sequences with good results.

Reproducing the smooth vocal tract trajectories is critical for high quality articulatory speech synthesis. This paper presents an adaptive neural control scheme for such a task using fuzzy logic and neural networks. The control scheme estimates motor commands from trajectories of flesh-points on selected articulators. These motor commands are then used to reproduce the trajectories of the underlying articulators in a 2nd order dynamical system. Initial experiments show that the control scheme is able to manipulate the mass-spring based elastic tract walls in a 2-dimensional articulatory synthesizer and to realize efficient speech motor control. The proposed controller achieves high accuracy during on-line tracking of the lips, the tongue, and the jaw in the simulation of consonant–vowel sequences. It also offers salient features such as generality and adaptability for future developments of control models in articulatory synthesis.