Deep Discriminative Embedding with Ranked Weight for Speaker Verification

Dao Zhou, Longbiao Wang, Kong Aik Lee, Meng Liu, Jianwu Dang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Deep speaker-embedding neural network trained with a discriminative loss function is widely known to be effective for speaker verification task. Notably, angular margin softmax loss, and its variants, were proposed to promote intra-class compactness. However, it is worth noticing that these methods are not effective enough in enhancing inter-class separability. In this paper, we present a ranked weight loss which explicitly encourages intra-class compactness and enhances inter-class separability simultaneously. During the neural network training process, the most attention is given to the target speaker in order to encourage intra-class compactness. Next, its nearest neighbor who has the greatest impact on the correct classification gets the second most attention while the least attention is paid to its farthest neighbor. Experimental results on VoxCeleb1, CN-Celeb and the Speakers in the Wild (SITW) core-core condition show that the proposed ranked weight loss achieves state-of-the-art performance.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030638221
Publication statusPublished - Nov 2020
Externally publishedYes
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference27th International Conference on Neural Information Processing, ICONIP 2020


  • Inter-class separability
  • Intra-class compactness
  • Speaker embedding
  • Speaker verification

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics


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