卷积 VocalTrax：用卷积替代时间反向传播的可微发音合成
Convolutional VocalTrax: Replacing Backpropagation Through Time with Convolution in Differentiable Articulatory Synthesis
Yongyi Zang, Debasish Ray Mohapatra, Randal Leistikow, Perry R. Cook
AI 速读 基于摘要
作者提出用因果卷积近似同一波导的频响，以替代可微发音合成中耗时的沿时间反向传播，从而加速 VocalTrax 的拟合。
cs.SD
查看原文摘要
VocalTrax recovers interpretable articulatory controls from a recording by gradient descent through a differentiable Pink Trombone synthesizer. Its vocal tract is a Kelly-Lochbaum waveguide updated twice per audio sample, so every gradient step backpropagates through a long recurrence, which is slow on GPUs. We propose Convolutional VocalTrax, which computes the exact frequency response of the same waveguide and applies it as a causal convolution. We assume that the tract is stationary within 5.8 ms blocks and that its lossy impulse response can be truncated after 93 ms. Gradients then pass through FFTs and a short recursion along the tract instead of through time. On 600 AudioMNIST recordings, the tract forward and backward pass runs 47 to 63 times faster on an A100 GPU, and a classifier trained only on real speech recognizes our reconstructions more often than those of VocalTrax (14.5% vs. 10.0%), although the log-mel distance to the target is 11% higher. Parameters fitted with our model keep this gain when rendered by the original VocalTrax (13.8%).