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。