Deep architectures for articulatory inversion
Benigno Uría, Iain Murray, Steve J. Renals, Korin Richmond · 2012
We implement two deep architectures for the acoustic-articulatory inversion mapping problem: a deep neural network and a deep trajectory mixture density network. We find that in both cases, deep architectures produce more accurate predic-tions than shallow architectures and that this is due to the higher expressive capability of a deep model and not a consequence of adding more adjustable parameters. We also find that a deep trajectory mixture density network is able to obtain better in-version accuracies than smoothing the results of a deep neural network. Our best model obtained an average root mean square error of 0.885 mm on the MNGU0 test dataset. Index Terms: Articulatory inversion, deep neural network, deep belief network, deep regression network, pretraining