Renals, S.

Richmond, K.

Uria, Benigno

English

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

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A Deep Neural Network for Acoustic-Articulatory Speech Inversion

A Deep Neural Network for Acoustic-Articulatory Speech Inversion

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