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Adversarial Data Augmentation for Robust Speaker Verification
This adversarial learning empowers the network to generate speaker embeddings that can deceive the augmentation classifier, making the learned speaker embeddings more robust in the face of augmentation variations.
Ordered and Binary Speaker Embedding
Modern speaker recognition systems represent utterances by embedding vectors.
MSV Challenge 2022: NPU-HC Speaker Verification System for Low-resource Indian Languages
Specifically, the weight transfer fine-tuning aims to constrain the distance of the weights between the pre-trained model and the fine-tuned model, which takes advantage of the previously acquired discriminative ability from the large-scale out-domain datasets and avoids catastrophic forgetting and overfitting at the same time.
Distinguishable Speaker Anonymization based on Formant and Fundamental Frequency Scaling
By directly scaling the formant and F0, the speaker distinguishability degradation of the anonymized speech caused by the introduction of other speakers is prevented.
