Trajectory training considering global variance for speech synthesis based on neural networks
Trajectory training considering global variance for speech synthesis based on neural networks
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DOI：
10.1109/icassp.2016.7472749
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Kei Hashimoto;Keiichiro Oura;Yoshihiko Nankaku;K. Tokuda
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文献类型：
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作者：
Kei Hashimoto;Keiichiro Oura;Yoshihiko Nankaku;K. Tokuda
提出了一种用于统计参数语音合成的深度神经网络(DNN)训练方法。最近，在统计参数语音合成中，DNN被用作声学模型，表示从语言特征到声学特征的映射函数。传统的基于DNN的语音合成存在以下问题：1)训练准则与合成准则不一致；2)生成的参数轨迹过度平滑。本文将考虑全局方差的参数轨迹生成过程引入到DNN的训练中。该方法可以得到一个统一的框架，在训练和综合中都使用相同的准则，并在考虑GV的情况下优化模型参数以生成参数。实验结果表明，该方法在合成语音的自然度方面优于传统方法。
This paper proposes a new training method of deep neural networks (DNNs) for statistical parametric speech synthesis. DNNs are recently used as acoustic models that represent mapping functions from linguistic features to acoustic features in statistical parametric speech synthesis. There are problems to be solved in conventional DNN-based speech synthesis: 1) the inconsistency between the training and synthesis criteria; and 2) the over-smoothing of the generated parameter trajectories. In this paper, we introduce the parameter trajectory generation process considering the global variance (GV) into the training of DNNs. A unified framework which consistently uses the same criterion in both training and synthesis can be obtained and the model parameters are optimized for parameter generation considering the GV in the proposed method. Experimental results show that the proposed method outperforms the conventional method in the naturalness of synthesized speech.