This paper presents an experimental study on deep speaker embedding with an
attention mechanism that has been found to be a powerful representation
learning technique in speaker recognition. In this framework, an attention
model works as a frame selector that computes an attention weight for each
frame-level feature vector, in accord with which an utterancelevel
representation is produced at the pooling layer in a speaker embedding network.
In general, an attention model is trained together with the speaker embedding
network on a single objective function, and thus those two components are
tightly bound to one another. In this paper, we consider the possibility that
the attention model might be decoupled from its parent network and assist other
speaker embedding networks and even conventional i-vector extractors. This
possibility is demonstrated through a series of experiments on a NIST Speaker
Recognition Evaluation (SRE) task, with 9.0% EER reduction and 3.8%
min_Cprimary reduction when the attention weights are applied to i-vector
extraction. Another experiment shows that DNN-based soft voice activity
detection (VAD) can be effectively combined with the attention mechanism to
yield further reduction of minCprimary by 6.6% and 1.6% in deep speaker
embedding and i-vector systems, respectively.

本文采用注意力机制实现深度说话人嵌入，将注意力模型作为帧选择器，计算每个帧级特征向量的注意力权重来产生说话人嵌入模型的汇聚层中的语音表征等内容，并在 NIST 演讲者识别评估 (SRE) 任务中进行 9.0％ EER 降低和 3.8％min_Cprimary 降低的实验，同时，还阐述了在深度说话人嵌入和 i 向量系统中结合 DNN 软语音活动检测 (VAD) 与注意力机制，将显著降低 minCprimary。