Recurrent Neural Networks (RNNs) have become the standard modeling technique for sequence data, and are used in a number of novel text-to-speech models. However, training a TTS model including RNN components has certain requirements for GPU performance and takes a long time. In contrast, studies have shown that CNN-based sequence synthesis technology can greatly reduce training time in text-to-speech models while ensuring a certain performance due to its high parallelism. We propose a new text-to-speech system based on deep convolutional neural networks that does not employ any RNN components (recurrent units). At the same time, we improve the generality and robustness of our model through a series of data augmentation methods such as Time Warping, Frequency Mask, and Time Mask. The final experimental results show that the TTS model using only the CNN component can reduce the training time compared to the classic TTS models such as Tacotron while ensuring the quality of the synthesized speech.

翻译：循环神经网络（RNN）已成为序列数据的标准建模技术，并被应用于多种新型文本转语音模型中。然而，包含RNN组件的TTS模型训练对GPU性能有一定要求，且耗时较长。相比之下，研究表明，基于CNN的序列合成技术因其高并行性，在保证一定性能的同时，可大幅减少文本转语音模型的训练时间。我们提出一种基于深度卷积神经网络的全新文本转语音系统，该系统不采用任何RNN组件（循环单元）。同时，我们通过时间扭曲、频率掩蔽和时间掩蔽等一系列数据增强方法，提升了模型的通用性和鲁棒性。最终实验结果表明，仅使用CNN组件的TTS模型在保证合成语音质量的同时，相较于Tacotron等经典TTS模型，训练时间显著缩短。