SEL

Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition

Speaker Representation Learning: Theories, Applications and Practice

I was invited to give a tutorial on speaker representation learning at the National Conference on Man-Machine Speech Communication (NCMMSC2023).

Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit

Context-aware Multimodal Fusion for Emotion Recognition

Interspeech 2022

DF-ResNet: Boosting Speaker Verification Performance with Depth-First Design

Interspeech 2022

On the Importance of Different Frequency Bins for Speaker Verification

Self-Knowledge Distillation via Feature Enhancement for Speaker Verification

Data Augmentation using Deep Generative Models for Embedding based Speaker Recognition

Data augmentation is an effective method to improve the robustness of embedding based speaker verification systems, which could be applied to either the front-end speaker embedding extractor or the back-end PLDA. Different from the conventional …

Text Adaptation for Speaker Verification with Speaker-Text Factorized Embeddings

We proposed the text-adptation speaker verification task and an intital solution called Speaker-text factorization network, which could deal with different text-mismatch conditions

Channel Invariant Speaker Embedding Learning with Joint Multi-Task and Adversarial Training

Using deep neural network to extract speaker embedding has significantly improved the speaker verification task. However, such embeddings are still vulnerable to channel variability. Previous works have used adversarial training to suppress channel …