The performance of speaker verification (SV) models may drop dramatically in
noisy environments. A speech enhancement (SE) module can be used as a front-end
strategy. However, existing SE methods may fail to bring performance
improvements to downstream SV systems due to artifacts in the predicted signals
of SE models. To compensate for artifacts, we propose a generic denoising
framework named LC4SV, which can serve as a pre-processor for various unknown
downstream SV models. In LC4SV, we employ a learning-based interpolation agent
to automatically generate the appropriate coefficients between the enhanced
signal and its noisy input to improve SV performance in noisy environments. Our
experimental results demonstrate that LC4SV consistently improves the
performance of various unseen SV systems. To the best of our knowledge, this
work is the first attempt to develop a learning-based interpolation scheme
aiming at improving SV performance in noisy environments.

在嘈杂环境中，为了提高演讲者验证（SV）模型的性能，我们提出了一个名为 LC4SV 的通用降噪框架，采用学习为基础的插值代理，自动生成增强信号与噪声输入之间合适的系数，从而改善 SV 系统的性能。

LC4SV：学习调节未见说话人验证模型的去噪框架

LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker  Verification Models

Implicit neural networks have been successfully used for surface
reconstruction from point clouds. However, many of them face scalability issues
as they encode the isosurface function of a whole object or scene into a single
latent vector. To overcome this limitation, a few approaches infer latent
vectors on a coarse regular 3D grid or on 3D patches, and interpolate them to
answer occupancy queries. In doing so, they loose the direct connection with
the input points sampled on the surface of objects, and they attach information
uniformly in space rather than where it matters the most, i.e., near the
surface. Besides, relying on fixed patch sizes may require discretization
tuning. To address these issues, we propose to use point cloud convolutions and
compute latent vectors at each input point. We then perform a learning-based
interpolation on nearest neighbors using inferred weights. Experiments on both
object and scene datasets show that our approach significantly outperforms
other methods on most classical metrics, producing finer details and better
reconstructing thinner volumes. The code is available at
this https URL

研究提出了一种使用点云卷积和在每个输入点计算潜在向量的方法，然后进行最近邻的基于学习的插值。实验结果表明，该方法在大多数经典指标上明显优于其他方法，生成更精细的细节并更好地重建了更薄的体积。