LPCNet is an efficient vocoder that combines linear prediction and deep
neural network modules to keep the computational complexity low. In this work,
we present two techniques to further reduce it's complexity, aiming for a
low-cost LPCNet vocoder-based neural Text-to-Speech (TTS) System. These
techniques are: 1) Sample-bunching, which allows LPCNet to generate more than
one audio sample per inference; and 2) Bit-bunching, which reduces the
computations in the final layer of LPCNet. With the proposed bunching
techniques, LPCNet, in conjunction with a Deep Convolutional TTS (DCTTS)
acoustic model, shows a 2.19x improvement over the baseline run-time when
running on a mobile device, with a less than 0.1 decrease in TTS mean opinion
score (MOS).

该研究提出了两种方法（Sample-bunching 和 Bit-bunching）来进一步降低 LPCNet 语音编解码器的复杂度，并与深度卷积 TTS 声学模型相结合，证明该技术在移动设备上可以实现 2.19 倍的运行时改进，同时 TTS 平均意见分数（MOS）只降低不到 0.1 分。

低成本神经文本转语音系统的 LPCNet 束缚声码器

Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems

Neural speech synthesis models have recently demonstrated the ability to
synthesize high quality speech for text-to-speech and compression applications.
These new models often require powerful GPUs to achieve real-time operation, so
being able to reduce their complexity would open the way for many new
applications. We propose LPCNet, a WaveRNN variant that combines linear
prediction with recurrent neural networks to significantly improve the
efficiency of speech synthesis. We demonstrate that LPCNet can achieve
significantly higher quality than WaveRNN for the same network size and that
high quality LPCNet speech synthesis is achievable with a complexity under 3
GFLOPS. This makes it easier to deploy neural synthesis applications on
lower-power devices, such as embedded systems and mobile phones.

提出 LPCNet，旨在改善语音合成的效率，与 WaveRNN 相比在相同的网络规模下实现了更高的质量，并且在 3GFLOPS 的复杂度下可以实现高质量的语音合成，可以在低功耗设备上更容易地部署神经合成应用，例如嵌入式系统和移动电话。