论文标题
紧凑的扬声器嵌入：LRX-vector
Compact Speaker Embedding: lrx-vector
论文作者
论文摘要
深度神经网络（DNN）最近被广泛用于说话者识别系统中，在各种基准测试中实现了最先进的性能。由于其出色的性能和可管理的计算复杂性，因此X-vector架构在该研究社区特别受欢迎。 In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD.在2019年的声音中，与全排名X矢量系统相比，我们能够将权重减少28％，同时保持识别率恒定（EER 1.83％）。
Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).
