6 citations · 15 across the 7 of their papers we have counts for
7 papers
Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
Zhaoyang Li, Jie Wang, XiaoXiao Li +4
In speaker diarization, traditional clustering-based methods remain widely used in real-world applications. However, these methods struggle with the complex distribution of speaker…
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization
Jie Wang, Zhicong Chen, Haodong Zhou +2
The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings…
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting
Jie Wang, Yuji Liu, Binling Wang +7
This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting. The diarization system obtains direction information of speaker by microp…
Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
SiCheng Yang, Methawee Tantrawenith, Haolin Zhuang +8
One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic. Existing works generally disentangle timbre, while informat…
The xmuspeech system for multi-channel multi-party meeting transcription challenge
Jie Wang, Yuji Liu, Binling Wang +7
This paper describes the system developed by the XMUSPEECH team for the Multi-channel Multi-party Meeting Transcription Challenge (M2MeT). For the speaker diarization task, we prop…
XMUSPEECH System for VoxCeleb Speaker Recognition Challenge 2021
Jie Wang, Fuchuang Tong, Zhicong Chen +3
This paper describes the XMUSPEECH speaker recognition and diarisation systems for the VoxCeleb Speaker Recognition Challenge 2021. For track 2, we evaluate two systems including R…
Adversarially learning disentangled speech representations for robust multi-factor voice conversion
Jie Wang, Jingbei Li, Xintao Zhao +3
Factorizing speech as disentangled speech representations is vital to achieve highly controllable style transfer in voice conversion (VC). Conventional speech representation learni…