Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Cheng, Luyao"'
Autor:
Cheng, Luyao, Wang, Hui, Zheng, Siqi, Chen, Yafeng, Huang, Rongjie, Zhang, Qinglin, Chen, Qian, Li, Xihao
Speaker diarization, the process of segmenting an audio stream or transcribed speech content into homogenous partitions based on speaker identity, plays a crucial role in the interpretation and analysis of human speech. Most existing speaker diarizat
Externí odkaz:
http://arxiv.org/abs/2408.12102
Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persisting challenge. In this paper, we propose a new self-supervised speaker verification approach, Self-Distillation Prototypes Networ
Externí odkaz:
http://arxiv.org/abs/2406.11169
Speaker verification systems experience significant performance degradation when tasked with short-duration trial recordings. To address this challenge, a multi-scale feature fusion approach has been proposed to effectively capture speaker characteri
Externí odkaz:
http://arxiv.org/abs/2406.02167
Autor:
Chen, Yafeng, Zheng, Siqi, Wang, Hui, Cheng, Luyao, Zhu, Tinglong, Huang, Rongjie, Deng, Chong, Chen, Qian, Zhang, Shiliang, Wang, Wen, Li, Xihao
We introduce 3D-Speaker-Toolkit, an open-source toolkit for multimodal speaker verification and diarization, designed for meeting the needs of academic researchers and industrial practitioners. The 3D-Speaker-Toolkit adeptly leverages the combined st
Externí odkaz:
http://arxiv.org/abs/2403.19971
Autor:
Cheng, Luyao, Zheng, Siqi, Zhang, Qinglin, Wang, Hui, Chen, Yafeng, Chen, Qian, Zhang, Shiliang
Speaker diarization has gained considerable attention within speech processing research community. Mainstream speaker diarization rely primarily on speakers' voice characteristics extracted from acoustic signals and often overlook the potential of se
Externí odkaz:
http://arxiv.org/abs/2309.10456
Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persisting challenge. In this paper, we propose a new self-supervised speaker verification approach, Self-Distillation Prototypes Networ
Externí odkaz:
http://arxiv.org/abs/2308.02774
Disentangling uncorrelated information in speech utterances is a crucial research topic within speech community. Different speech-related tasks focus on extracting distinct speech representations while minimizing the affects of other uncorrelated inf
Externí odkaz:
http://arxiv.org/abs/2306.15354
Speaker diarization(SD) is a classic task in speech processing and is crucial in multi-party scenarios such as meetings and conversations. Current mainstream speaker diarization approaches consider acoustic information only, which result in performan
Externí odkaz:
http://arxiv.org/abs/2305.12927
Effective fusion of multi-scale features is crucial for improving speaker verification performance. While most existing methods aggregate multi-scale features in a layer-wise manner via simple operations, such as summation or concatenation. This pape
Externí odkaz:
http://arxiv.org/abs/2305.12838
Time delay neural network (TDNN) has been proven to be efficient for speaker verification. One of its successful variants, ECAPA-TDNN, achieved state-of-the-art performance at the cost of much higher computational complexity and slower inference spee
Externí odkaz:
http://arxiv.org/abs/2303.00332