325 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Alibaba Group (United States)US30 papers
- Microsoft (United States)US20 papers
- University of OxfordGB19 papers
- Alibaba Group (China)CN14 papers
- Goddard Space Flight CenterUS14 papers
- Massachusetts Institute of TechnologyUS14 papers
- Search for Extraterrestrial IntelligenceUS14 papers
- Eötvös Loránd UniversityHU13 papers
- HUN-REN Research Centre for Astronomy and Earth SciencesHU13 papers
- Konkoly ObservatoryHU13 papers
- University of SzegedHU13 papers
- Tencent (China)CN12 papers
17 papers · 1 filter
SpatialEmb: Extract and Encode Spatial Information for 1-Stage Multi-channel Multi-speaker ASR on Arbitrary Microphone Arrays
Yiwen Shao, Yong Xu, Sanjeev Khudanpur +1
Spatial information is a critical clue for multi-channel multi-speaker target speech recognition. Most state-of-the-art multi-channel Automatic Speech Recognition (ASR) systems ext…
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification
Chunlei Zhang, Dong Yu
Self-supervised learning (SSL) has drawn an increased attention in the field of speech processing. Recent studies have demonstrated that contrastive learning is able to learn discr…
Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE
Jiachen Lian, Chunlei Zhang, Gopala Krishna Anumanchipalli +1
In this paper, we propose a novel unsupervised text-to-speech acoustic model training scheme, named UTTS, which does not require text-audio pairs. UTTS is a multi-speaker speech sy…
Towards Robust Speaker Verification with Target Speaker Enhancement
Chunlei Zhang, Meng Yu, Chao Weng +1
This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embeddin…
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation
Max W. Y. Lam, Jun Wang, Dan Su +1
One of the leading single-channel speech separation (SS) models is based on a TasNet with a dual-path segmentation technique, where the size of each segment remains unchanged throu…
Contrastive Separative Coding for Self-supervised Representation Learning
Jun Wang, Max W. Y. Lam, Dan Su +1
To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our…
Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks
Max W. Y. Lam, Jun Wang, Dan Su +1
Recent research on the time-domain audio separation networks (TasNets) has brought great success to speech separation. Nevertheless, conventional TasNets struggle to satisfy the me…
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…
Audio-visual Multi-channel Integration and Recognition of Overlapped Speech
Jianwei Yu, Shi-Xiong Zhang, Bo Wu +6
Automatic speech recognition (ASR) technologies have been significantly advanced in the past few decades. However, recognition of overlapped speech remains a highly challenging tas…
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…
Multi-Channel Speaker Verification for Single and Multi-talker Speech
Saurabh Kataria, Shi-Xiong Zhang, Dong Yu
To improve speaker verification in real scenarios with interference speakers, noise, and reverberation, we propose to bring together advancements made in multi-channel speech featu…
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition
Liqiang He, Dan Su, Dong Yu
In this paper, we explore the neural architecture search (NAS) for automatic speech recognition (ASR) systems. With reference to the previous works in the computer vision field, th…
Audio-visual Recognition of Overlapped speech for the LRS2 dataset
Jianwei Yu, Shi-Xiong Zhang, Jian Wu +7
Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of…
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition
Zhao You, Dan Su, Jie Chen +2
Self-attention networks (SAN) have been introduced into automatic speech recognition (ASR) and achieved state-of-the-art performance owing to its superior ability in capturing long…
Mixup-breakdown: a consistency training method for improving generalization of speech separation models
Max W. Y. Lam, Jun Wang, Dan Su +1
Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditi…