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%.
Index Terms: Bidirectional Neural Networks (BNNs), Feed-Forward Networks (FFNs), Time Delay Neural Networks (TDNNs), MOCHA-TIMIT database, Acoustic-articulatory inversion mapping
Cite as: Behbood, H., Seyyedsalehi, S.A., Tohidypour, H.R. (2010) A new bidirectional neural network model for the acoustic- articulatory inversion mapping for speech recognition. Proc. Speech Prosody 2010, paper 580
@inproceedings{behbood10_speechprosody,
author={Hossein Behbood and Seyyed Ali Seyyedsalehi and Hamid Reza Tohidypour},
title={{A new bidirectional neural network model for the acoustic- articulatory inversion mapping for speech recognition}},
year=2010,
booktitle={Proc. Speech Prosody 2010},
pages={paper 580}
}