Utterance-Level Aggregation For Speaker Recognition In The Wild

本文使用NetVLAD，将frame-level聚合为utterance-level。
in the wild: 4s以上的语音

实现流程

将通过Thin ResNet的frame-level通过NetVLAD聚合为utterance-level。
网络输入为R（257×T×1），输出变为了R（1×T/32×512）

NetVLAD: 输出一个K×D的矩阵V，K为聚类类别数，D为每一类的维数。

第一项代表了这一帧特征在类别k的权重，第二项代表了其与类中心的残差。
最后将每帧向量L2标准化后连接起来。

在GhostVLAD中，一些类并不参与最后的连接，因此可以剔除一些噪声段

实验

在GhostVLAD中，聚类数与损失函数对结果影响不大。

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