State-of-the-art speaker recognition systems comprise a speaker embedding front-end
followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness …

Cross-lingual personalized speech generation seeks to synthesize a target speaker's voice
from only a few training samples that are in a different language. One popular technique is to …

Domain mismatch problem caused by speaker-unrelated feature has been a major topic in
speaker recognition. In this paper, we propose an explicit disentanglement framework to …

Since most of the conventional domain adaptation models for Speaker Verification (SV) task
apply adaptation from single source domain to the target domain, they may not be robust …

Multi-genre speaker recognition is becoming increasingly popular due to its ability to better
represent the complexities of real-world applications. However, a major challenge is the …

In the real world, speaker recognition systems usually suffer from serious performance
degradation due to the domain mismatch between training and test conditions. To alleviate …

In real-world applications, speaker recognition models often face various domain-mismatch
challenges, leading to a significant drop in performance. Although numerous domain …

Data augmentation (DA) has gained widespread popularity in deep speaker models due to
its ease of implementation and significant effectiveness. It enriches training data by …

Despite the great success of speaker recognition models based on deep neural networks,
deploying a pre-trained model in real-world scenarios often leads to significant performance …

Deploying a well-optimized pre-trained speaker recognition model in a new domain often
leads to a significant decline in performance. While fine-tuning is a commonly employed …