The article presents a method for adapting a GMM-based acousticarticulatory inversion model trained on a reference speaker to another speaker. The goal is to estimate the articulatory trajectories in the geometrical space of a reference speaker from the speech audio signal of another speaker. This method is developed in the context of a system of visual biofeedback, aimed at pronunciation training. This system provides a speaker with visual information about his/her own articulation, via a 3D orofacial clone. In previous work, we proposed to use GMM-based voice conversion for speaker adaptation. Acoustic-articulatory mapping was achieved in 2 consecutive steps: 1) converting the spectral trajectories of the target speaker (i.e. the system user) into spectral trajectories of the reference speaker (voice conversion), and 2) estimating the most likely articulatory trajectories of the reference speaker from the converted spectral features (acoustic-articulatory inversion). In this work, we propose to combine these two steps into the same statistical mapping framework, by fusing multiple regressions based on trajectory GMM and maximum likelihood criterion (MLE). The proposed technique is compared to two standard speaker adaptation techniques based respectively on MAP and MLLR.
Cite as: Hueber, T., Bailly, G., Badin, P., Elisei, F. (2013) Speaker adaptation of an acoustic-articulatory inversion model using cascaded Gaussian mixture regressions. Proc. Interspeech 2013, 2753-2757, doi: 10.21437/Interspeech.2013-631
@inproceedings{hueber13b_interspeech,
author={Thomas Hueber and Gérard Bailly and Pierre Badin and Frédéric Elisei},
title={{Speaker adaptation of an acoustic-articulatory inversion model using cascaded Gaussian mixture regressions}},
year=2013,
booktitle={Proc. Interspeech 2013},
pages={2753--2757},
doi={10.21437/Interspeech.2013-631}
}