The paper introduces ESPnet-SPK, a toolkit for training speaker embedding extractors. It offers an open-source platform for the speaker recognition community to build models, ranging from x-vector to recent SKA-TDNN….
Continue readingThe article presents a novel paradigm, Self-supervised Reflective Learning (SSRL), for speaker representation learning. SSRL integrates self-supervised knowledge distillation with online clustering to refine pseudo labels and train the model…
Continue readingThe article introduces a new loss function for speaker recognition with deep neural networks, based on Jeffreys Divergence. This divergence, added to the cross-entropy loss function, allows for maximizing the…
Continue readingThe article explores the role of deep neural networks (DNNs) in automatic speaker recognition, particularly in modeling supra-segmental temporal information (SST). Despite their success, the understanding of what contributes to…
Continue readingThe study presents a novel test to measure how much the performance of neural networks in speaker recognition can be attributed to the modeling of supra-segmental temporal features (SST). It…
Continue readingThe paper presents a new dataset, the Large Replay Parallel Dataset (LRPD), designed to improve the generalization ability of deep neural networks (DNNs) in detecting replay attacks in voice anti-spoofing….
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