State-of-the-art speaker verification frameworks have typically focused on
developing models with increasingly deeper (more layers) and wider (number of
channels) models to improve their verification performance. Instead, this paper
proposes an approach to increase the model resolution capability using
attention-based dynamic kernels in a convolutional neural network to adapt the
model parameters to be feature-conditioned. The attention weights on the
kernels are further distilled by channel attention and multi-layer feature
aggregation to learn global features from speech. This approach provides an
efficient solution to improving representation capacity with lower data
resources. This is due to the self-adaptation to inputs of the structures of
the model parameters. The proposed dynamic convolutional model achieved 1.62\%
EER and 0.18 miniDCF on the VoxCeleb1 test set and has a 17\% relative
improvement compared to the ECAPA-TDNN using the same training resources.

本文提出一种利用卷积神经网络中的基于注意力的动态卷积核来提高模型分辨率能力的方法，将模型参数适应于特征条件，通过通道注意力和多层特征聚合进一步提取全局语音特征，从而提高特征表示效果。实验结果表明，该方法在使用较少数据的情况下取得了良好的识别效果。

低资源说话者验证的动态核和通道注意力

Dynamic Kernels and Channel Attention for Low Resource Speaker Verification

In this work we analyze strategies for convolutional neural network scaling;
that is, the process of scaling a base convolutional network to endow it with
greater computational complexity and consequently representational power.
Example scaling strategies may include increasing model width, depth,
resolution, etc. While various scaling strategies exist, their tradeoffs are
not fully understood. Existing analysis typically focuses on the interplay of
accuracy and flops (floating point operations). Yet, as we demonstrate, various
scaling strategies affect model parameters, activations, and consequently
actual runtime quite differently. In our experiments we show the surprising
result that numerous scaling strategies yield networks with similar accuracy
but with widely varying properties. This leads us to propose a simple fast
compound scaling strategy that encourages primarily scaling model width, while
scaling depth and resolution to a lesser extent. Unlike currently popular
scaling strategies, which result in about $O(s)$ increase in model activation
w.r.t. scaling flops by a factor of $s$, the proposed fast compound scaling
results in close to $O(\sqrt{s})$ increase in activations, while achieving
excellent accuracy. This leads to comparable speedups on modern memory-limited
hardware (e.g., GPU, TPU). More generally, we hope this work provides a
framework for analyzing and selecting scaling strategies under various
computational constraints.

本文探讨卷积神经网络的扩展策略，指对基础卷积网络进行扩展以赋予其更高的表征能力。作者提出一种快速且简单的复合扩展策略，旨在主要扩展模型的宽度，而稍微扩展其深度和分辨率。实验结果表明，许多扩展策略可以产生类似的精度，但其性能却各异。该研究为在各种计算约束下分析和选择缩放策略提供了框架。