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Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model by Tomoki Toda; Alan W. Black; Keiichi Tokuda is a Computer Science article available to read on EtoBox.

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In this paper, we describe a statistical approach to both an articulatory-to-acoustic mapping and an acoustic-to-articulatory inversion mapping without using phonetic information. The joint probability density of an articulatory parameter and an acoustic parameter is modeled using a Gaussian mixture model (GMM) based on a parallel acoustic-articulatory speech database. We apply the GMM-based mapping using the minimum mean-square error (MMSE) criterion, which has been proposed for voice conversion, to the two mappings. Moreover, to improve the mapping performance, we apply maximum likelihood estimation (MLE) to the GMM-based mapping method. The determination of a target parameter trajectory having appropriate static and dynamic properties is obtained by imposing an explicit relationship between static and dynamic features in the MLE-based mapping. Experimental results demonstrate that the MLE-based mapping with dynamic features can significantly improve the mapping performance compared with the MMSE-based mapping in both the articulatory-to-acoustic mapping and the inversion mapping.

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Author

Tomoki Toda; Alan W. Black; Keiichi Tokuda

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0167-6393)

Published

2008

Language

EN

Field

Computer Science (Physical Sciences)