94 citations · 305 across the 37 of their papers we have counts for
11 papers · 1 filter
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Zhuohuang Zhang, Yong Xu, Meng Yu +4
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are har…
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning
Wei Xia, Chunlei Zhang, Chao Weng +2
In this study, we investigate self-supervised representation learning for speaker verification (SV). First, we examine a simple contrastive learning approach (SimCLR) with a moment…
WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation
Zhaoheng Ni, Yong Xu, Meng Yu +4
This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recogni…
Improving RNN Transducer With Target Speaker Extraction and Neural Uncertainty Estimation
Jiatong Shi, Chunlei Zhang, Chao Weng +3
Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers. This work presents a joint framewo…
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
Aswin Shanmugam Subramanian, Chao Weng, Shinji Watanabe +4
This paper proposes a new paradigm for handling far-field multi-speaker data in an end-to-end neural network manner, called directional automatic speech recognition (D-ASR), which…
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Daniel Michelsanti, Zheng-Hua Tan, Shi-Xiong Zhang +4
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generat…