A New Bidirectional Neural Network Model for the Acoustic-Articulatory Inversion Mapping For Speech Recognition

Hossein Behbood, Seyyed Ali Seyyedsalehi, Hamid Reza Tohidypour, Amirkabir University of Technology (Tehran Polytechnic)

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-inverse 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 the articulatory features. In comparison with a standard model, the output of bidirectional model as auxiliary data in phone recognition process, increases the accuracy up to approximately 3\%.