We present Deep Voice, a production-quality text-to-speech system constructed
entirely from deep neural networks. Deep Voice lays the groundwork for truly
end-to-end neural speech synthesis. The system comprises five major building
blocks: a segmentation model for locating phoneme boundaries, a
grapheme-to-phoneme conversion model, a phoneme duration prediction model, a
fundamental frequency prediction model, and an audio synthesis model. For the
segmentation model, we propose a novel way of performing phoneme boundary
detection with deep neural networks using connectionist temporal classification
(CTC) loss. For the audio synthesis model, we implement a variant of WaveNet
that requires fewer parameters and trains faster than the original. By using a
neural network for each component, our system is simpler and more flexible than
traditional text-to-speech systems, where each component requires laborious
feature engineering and extensive domain expertise. Finally, we show that
inference with our system can be performed faster than real time and describe
optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x
speedups over existing implementations.

Deep Voice 是一个使用深度神经网络构建的优秀的文本到语音系统，由五个主要组成部分构成，包括用于定位语音边界的分段模型、字素到音素转换模型、音素持续时间预测模型、基频预测模型和音频合成模型。通过使用神经网络进行每个组件的构建，既简单又灵活。此外，我们通过优化后的 WaveNet 推断内核，实现了 400 倍的实时速度。