论文标题
使用简单的注意模块和基于添加角缘损失和元学习的简单注意模块和关节优化的音频反动
Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning
论文作者
论文摘要
自动扬声器验证系统容易受到各种访问威胁的影响，促使研究有效的欺骗检测系统，以充当过滤出这种欺骗攻击的大门。这项研究引入了一个简单的注意模块，以推断卷积层中特征图的3 dim注意力重量，然后优化了能量函数以确定每个神经元的重要性。随着语音转换和语音合成技术的发展，看不见的欺骗攻击正在不断出现，以限制欺骗检测系统的性能。在这里，我们提出了一种基于二进制分类的加权添加角度损失的联合优化方法，并采用元学习训练框架来开发一个有效的系统，该系统对模型概括增强的广泛欺骗攻击具有强大的态度。结果，与当前的最新系统相比，我们提出的方法可提供竞争结果，汇总EER为0.99％，最小T-DCF为0.0289。
Automatic speaker verification systems are vulnerable to a variety of access threats, prompting research into the formulation of effective spoofing detection systems to act as a gate to filter out such spoofing attacks. This study introduces a simple attention module to infer 3-dim attention weights for the feature map in a convolutional layer, which then optimizes an energy function to determine each neuron's importance. With the advancement of both voice conversion and speech synthesis technologies, unseen spoofing attacks are constantly emerging to limit spoofing detection system performance. Here, we propose a joint optimization approach based on the weighted additive angular margin loss for binary classification, with a meta-learning training framework to develop an efficient system that is robust to a wide range of spoofing attacks for model generalization enhancement. As a result, when compared to current state-of-the-art systems, our proposed approach delivers a competitive result with a pooled EER of 0.99% and min t-DCF of 0.0289.
