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In this work, we implement a deep belief network for the acoustic-articulatory inversion mapping problem. We find that adding up to 3 hidden-layers improves inversion accuracy. We also show that this improvement is due to the higher ex- pressive capability of a deep model and not a consequence of adding more adjustable parameters. Additionally, we show unsupervised pretraining of the sys- tem improves its performance in all cases, even for a 1 hidden-layer model. Our implementation obtained an average root mean square error of 0.95 mm on the MNGU0 test dataset, beating all previously published results.
|Title of host publication||Proc. NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning|
|Publication status||Published - Dec 2011|
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- 2 Finished
Richmond, K., Renals, S., Cleland, J. & Scobbie, J. M.
1/02/11 → 31/07/14