@inproceedings{Uria2012,
author = {Benigno Uria and Iain Murray and Steve Renals and
Korin Richmond},
title = {Deep Architectures for Articulatory Inversion},
booktitle = {Proc. Interspeech},
address = {Portland, Oregon, USA},
abstract = { 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 predictions 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 inversion 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.},
categories = {Articulatory inversion, deep neural network, deep
belief network, deep regression network, pretraining},
keywords = {Articulatory inversion, deep neural network, deep
belief network, deep regression network, pretraining},
month = sep,
pdf = {http://http://www.benignouria.com/en/research/papers/Uria2012.pdf},
year = 2012
}