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Found 3 papers, 2 papers with code
Leveraging speaker attribute information using multi task learning for speaker verification and diarization
On a test set of US Supreme Court recordings, we show that by leveraging two additional forms of speaker attribute information derived respectively from the matched training data, and VoxCeleb corpus, we improve the performance of our deep speaker embeddings for both verification and diarization tasks, achieving a relative improvement of 26. 2% in DER and 6. 7% in EER compared to baselines using speaker labels only.
DropClass and DropAdapt: Dropping classes for deep speaker representation learning
The first proposed method, DropClass, works via periodically dropping a random subset of classes from the training data and the output layer throughout training, resulting in a feature extractor trained on many different classification tasks.
Channel adversarial training for speaker verification and diarization
Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong.
