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Multi-Speaker End-to-End Multi-Modal Speaker Diarization System for the MISP 2022 Challenge.

, , , and . ICASSP, page 1-2. IEEE, (2023)

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WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing., , , , , , , , , and 8 other author(s). CoRR, (2021)Self-Knowledge Distillation via Feature Enhancement for Speaker Verification., , , , and . ICASSP, page 7542-7546. IEEE, (2022)Leveraging In-the-Wild Data for Effective Self-Supervised Pretraining in Speaker Recognition., , , , , , , and . CoRR, (2023)Dual Path Embedding Learning for Speaker Verification with Triplet Attention., , and . INTERSPEECH, page 291-295. ISCA, (2022)Enroll-Aware Attentive Statistics Pooling for Target Speaker Verification., , and . INTERSPEECH, page 311-315. ISCA, (2022)Leveraging in-the-wild Data for Effective Self-supervised Pretraining in Speaker Recognition., , , , , , , and . ICASSP, page 10901-10905. IEEE, (2024)Attention-Based Encoder-Decoder End-to-End Neural Diarization With Embedding Enhancer., , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2024)SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022., , , , , and . CoRR, (2022)A Comprehensive Study on Self-Supervised Distillation for Speaker Representation Learning., , , , and . SLT, page 599-604. IEEE, (2022)A Statistical-Feature ML Approach to IP Traffic Classification Based on CUDA., , , , and . Trustcom/BigDataSE/ISPA, page 2235-2239. IEEE, (2016)