A novel neural-based model for acoustic-articulatory inversion mapping

In this paper, a new bidirectional neural network for better acoustic-articulatory inversion mapping is proposed. The model is motivated by the parallel structure of human brain, processing information by having forward--reverse connections. In other words, there would be a feedback from articulatory system to the acoustic signals emitted from that organ. Inspired by this mechanism, a new bidirectional model is developed to map speech representations to articulatory features. Formation of attractor dynamics in such bidirectional model is first carried out by training the reference speaker subspace as the continuous attractor. Then, it is used to recognize the other speaker’s speech. In fact, the structure and training of this bidirectional model is designed in such a way that the network learns to denoise the signal step by step, using properties of attractors it has formed. In this work, the efficiency of a nonlinear feedforward network is compared to the same one with a bidirectional connection. The bidirectional model increases the accuracy up to approximately 3% (from 62.09 to 64.91%) in the phone recognition process.