Data driven articulatory synthesis with deep neural networks ?
We present an articulatory-to-acoustic mapping for real-time articulatory synthesis. The method uses a deep neural network with a tapped-delay input line. Tapped-delay line efficiently captures dynamics in articulatory trajectories. The model achieved higher accuracy than competing models based on Gaussian mixtures. The improvement was also found perceivable in a subjective listening test.
articulatory synthesis electromagnetic articulography deep learning gaussian mixture models