  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.
