Compact Speaker Embedding: lrx-Vector

Munir Georges, Jonathan Huang, Tobias Bocklet


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).


 DOI: 10.21437/Interspeech.2020-2106

Cite as: Georges, M., Huang, J., Bocklet, T. (2020) Compact Speaker Embedding: lrx-Vector. Proc. Interspeech 2020, 3236-3240, DOI: 10.21437/Interspeech.2020-2106.


@inproceedings{Georges2020,
  author={Munir Georges and Jonathan Huang and Tobias Bocklet},
  title={{Compact Speaker Embedding: lrx-Vector}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={3236--3240},
  doi={10.21437/Interspeech.2020-2106},
  url={http://dx.doi.org/10.21437/Interspeech.2020-2106}
}