Audiovisual articulatory inversion based on Gaussian Mixture Model (GMM)

I. Ozbek, M. Demirekler
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Abstract

In this study, we examined articulatory inversion using audiovisual information based on Gaussian Mixture Model (GMM). In this method the joint distribution of the articulatory movement and audio (and/or visual) data are modelled via a mixture of Gaussians. The conditional expected value of the GMM is used as regression function between the audio (and/orvisual) and ar-ticulatory spaces. We also examined various fusion methods in order to combine acoustic and visual information in articula-tory inversion. The fusion methods improve the performance of articulatory inversion.
基于高斯混合模型(GMM)的视听发音反演
在这项研究中,我们研究了基于高斯混合模型(GMM)的视听信息的发音反转。在这种方法中,发音运动和音频(和/或视觉)数据的联合分布通过混合高斯分布进行建模。GMM的条件期望值被用作音频(和/或视觉)和听觉空间之间的回归函数。我们还研究了各种融合方法,以便在关节倒置中结合声学和视觉信息。融合方法提高了关节内翻的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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