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Channel Adversarial Training for Speaker Verification and Diarization

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Original languageEnglish
Title of host publicationProceedings of the 45th International Conference on Acoustics, Speech, and Signal Processing
Number of pages5
Publication statusAccepted/In press - 24 Jan 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020
Conference number: 45

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20

Abstract

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.

    Research areas

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

Event

2020 IEEE International Conference on Acoustics, Speech, and Signal Processing

4/05/208/05/20

Barcelona, Spain

Event: Conference

ID: 137077237