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Title:Modelling the uncertainty in recovering articulation from acoustics.
Authors: Korin Richmond ; Simon King ; Paul Taylor
Date:Jul 2003
Publication Title:Computer Speech and Language
Publication Type:Journal Article Publication Status:Published
Volume No:17(2-3) Page Nos:153-172
DOI:10.1016/S0885-2308(03)00005-6 ISBN/ISSN:1095-8363
This paper presents an experimental comparison of the performance of the multilayer perceptron (MLP) with that of the mixture density network (MDN) for an acoustic-to-articulatory mapping task. A corpus of acoustic-articulatory data recorded by electromagnetic articulography (EMA) for a single speaker was used as training and test data for this purpose. In theory, the MDN is able to provide a richer, more flexible description of the target variables in response to a given input vector than the least-squares trained MLP. Our results show that the mean likelihoods of the target articulatory parameters for an unseen test set were indeed consistently higher with the MDN than with the MLP. The increase ranged from approximately 3% to 22%, depending on the articulatory channel in question. On the basis of these results, we argue that using a more flexible description of the target domain, such as that offered by the MDN, can prove beneficial when modelling the acoustic-to-articulatory mapping.
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Bibtex format
author = { Korin Richmond and Simon King and Paul Taylor },
title = {Modelling the uncertainty in recovering articulation from acoustics.},
journal = {Computer Speech and Language},
publisher = {Elsevier},
year = 2003,
month = {Jul},
volume = {17(2-3)},
pages = {153-172},
doi = {10.1016/S0885-2308(03)00005-6},
url = {},

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