Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model

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...

• Type: Journal
• Year: 2008
• Where: SPEECH
• Authors: Tomoki Toda, Alan W. Black, Keiichi Tokuda
• Keywords: Acoustic-to-articulatory Inversion Mapping, Articulatory-to-acoustic Mapping, Inversion Mapping, SPEECH 2008