改进了用于实时语音到嘴唇转换的最小转换轨迹误差训练

Wei Han, Lijuan Wang, F. Soong, Bo Yuan
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引用次数: 7

摘要

基于高斯混合模型(GMM)的语音到嘴唇转换通常有两种可选的方式:批量转换和基于滑动窗口的实时处理转换。为了提高批量转换的性能,以前提出了最小转换轨迹误差(MCTE)训练。在本文中,我们扩展了之前的工作,提出了一个新的训练准则,MCTE实时转换(R-MCTE),以显式优化基于滑动窗口的转换质量。在R-MCTE中,我们使用概率下降方法通过最小化实时转换视觉轨迹对训练数据的误差来优化模型参数。对LIPS 2008视觉语音合成挑战赛数据集的客观评价表明,该方法既具有良好的嘴唇动画性能,又具有较低的实时转换延迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved minimum converted trajectory error training for real-time speech-to-lips conversion
Gaussian mixture model (GMM) based speech-to-lips conversion often operates in two alternative ways: batch conversion and sliding window-based conversion for real-time processing. Previously, Minimum Converted Trajectory Error (MCTE) training has been proposed to improve the performance of batch conversion. In this paper, we extend previous work and propose a new training criteria, MCTE for Real-time conversion (R-MCTE), to explicitly optimize the quality of sliding window-based conversion. In R-MCTE, we use the probabilistic descent method to refine model parameters by minimizing the error on real-time converted visual trajectories over training data. Objective evaluations on the LIPS 2008 Visual Speech Synthesis Challenge data set shows that the proposed method achieves both good lip animation performance and low delay in real-time conversion.
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