Channel Adversarial Training for Speaker Verification and Diarization

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong. We propose a training strategy which aims to produce features that are invariant at the granularity of the recording or channel, a finer grained objective than dataset- or environment- invariance. By training an adversary to predict whether pairs of same-speaker embeddings belong to the same recording in a Siamese fashion, learned features are discouraged from utilizing channel information that may be speaker discriminative during training. Experiments for verification on VoxCeleb and diarization and verification on CALLHOME show promising improvements over a strong baseline in addition to outperforming a dataset-adversarial model. The VoxCeleb model in particular performs well, achieving a 4% relative improvement in EER over a Kaldi baseline, while using a similar architecture and less training data.
Original languageEnglish
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-5090-6631-5
ISBN (Print)978-1-5090-6632-2
Publication statusPublished - 14 May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020
Conference number: 45

Publication series

ISSN (Print)1520-6149
ISSN (Electronic)2379-190X


Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2020


  • Speaker verification
  • diarization
  • domain adversarial training
  • adversarial learning
  • deep neural network

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