In the last two years, there have been numerous papers that have looked into using Deep Neural Networks to replace the acoustic model in traditional statistical parametric speech synthesis. However, far less attention has been paid to approaches like DNN-based postfiltering where DNNs work in conjunction with traditional acoustic models. In this paper, we investigate the use of Recurrent Neural Networks as a potential postfilter for synthesis. We explore the possibility of replacing existing postfilters, as well as highlight the ease with which arbitrary new features can be added as input to the postfilter. We also tried a novel approach of jointly training the Classification And Regression Tree and the postfilter, rather than the traditional approach of training them independently.

翻译：在过去的两年里,有许多论文研究利用深神经网络来取代传统统计参数语言合成的声学模型,然而,对DNN的后过滤法等方法的关注却少得多,DNN的后过滤法与传统的声学模型一起工作。在本文件中,我们调查了经常神经网络作为可能的后过滤器进行合成的可能性。我们探索了替换现有后过滤器的可能性,并突出强调了可以轻松地添加任意的新特征作为后过滤器的投入。我们还尝试了联合培训分类和回归树和后过滤器的新方法,而不是独立培训它们的传统方法。