80 citations · 242 across the 20 of their papers we have counts for
16 papers · 1 filter
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Meng Yu, Yong Xu, Chunlei Zhang +2
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when…
MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment
Meng Yu, Chunlei Zhang, Yong Xu +2
The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech q…
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
Helin Wang, Bo Wu, Lianwu Chen +7
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments. We propose…
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…
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…