Research

  • Uria, Benigno (2011): A deep belief network for the acoustic-articulatory inversion mapping problem. University of Edinburgh, School of Informatics, Diss.

    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 this is due to the higher expressive capability of a deep model and not a consequence of adding more adjustable parameters. Besides, we show unsupervised pretraining of the system 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.