In this paper, we propose a novel conditional convolution network, named
location-variable convolution, to model the dependencies of the waveform
sequence. Different from the use of unified convolution kernels in WaveNet to
capture the dependencies of arbitrary waveform, the location-variable
convolution uses convolution kernels with different coefficients to perform
convolution operations on different waveform intervals, where the coefficients
of kernels is predicted according to conditioning acoustic features, such as
Mel-spectrograms. Based on location-variable convolutions, we design LVCNet for
waveform generation, and apply it in Parallel WaveGAN to design more efficient
vocoder. Experiments on the LJSpeech dataset show that our proposed model
achieves a four-fold increase in synthesis speed compared to the original
Parallel WaveGAN without any degradation in sound quality, which verifies the
effectiveness of location-variable convolutions.

本文提出了一种名为位置可变卷积的新型条件卷积网络来建模波形序列的依赖关系，其使用具有不同系数的卷积核对不同的波形区间进行卷积操作，该系数根据条件声学特征（如 Mel - 频谱图）预测，基于该方法，我们设计了 LVCNet 进行波形生成，并将其应用于 Parallel WaveGAN 中来设计更高效的声码器，LJSpeech 数据集上的实验结果表明，与原始的 Parallel WaveGAN 相比，我们提出的模型在不降低声音质量的前提下实现了四倍的合成速度提高，从而验证了位置可变卷积的有效性。