6 citations · 9 across the 7 of their papers we have counts for
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UniPET-SPK: A Unified Framework for Parameter-Efficient Tuning of Pre-trained Speech Models for Robust Speaker Verification
Mufan Sang, John H. L. Hansen
With excellent generalization ability, SSL speech models have shown impressive performance on various downstream tasks in the pre-training and fine-tuning paradigm. However, as the…
Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification
Mufan Sang, John H. L. Hansen
With excellent generalization ability, self-supervised speech models have shown impressive performance on various downstream speech tasks in the pre-training and fine-tuning paradi…
Improving Transformer-based Networks With Locality For Automatic Speaker Verification
Mufan Sang, Yong Zhao, Gang Liu +2
Recently, Transformer-based architectures have been explored for speaker embedding extraction. Although the Transformer employs the self-attention mechanism to efficiently model th…
Multi-Frequency Information Enhanced Channel Attention Module for Speaker Representation Learning
Mufan Sang, John H. L. Hansen
Recently, attention mechanisms have been applied successfully in neural network-based speaker verification systems. Incorporating the Squeeze-and-Excitation block into convolutiona…
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization
Mufan Sang, Haoqi Li, Fang Liu +2
Training speaker-discriminative and robust speaker verification systems without speaker labels is still challenging and worthwhile to explore. In this study, we propose an effectiv…
DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning
Mufan Sang, Wei Xia, John H. L. Hansen
Despite speaker verification has achieved significant performance improvement with the development of deep neural networks, domain mismatch is still a challenging problem in this f…
Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias
Mufan Sang, Wei Xia, John H. L. Hansen
In forensic applications, it is very common that only small naturalistic datasets consisting of short utterances in complex or unknown acoustic environments are available. In this…