58 citations · 335 across the 31 of their papers we have counts for
9 papers · 1 filter
Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction
Heming Wang, Yao Qian, Xiaofei Wang +6
Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a…
Location-based training for multi-channel talker-independent speaker separation
Hassan Taherian, Ke Tan, DeLiang Wang
Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial informatio…
VoiceFixer: Toward General Speech Restoration with Neural Vocoder
Haohe Liu, Qiuqiang Kong, Qiao Tian +4
Speech restoration aims to remove distortions in speech signals. Prior methods mainly focus on single-task speech restoration (SSR), such as speech denoising or speech declipping.…
Multichannel Speech Enhancement without Beamforming
Asutosh Pandey, Buye Xu, Anurag Kumar +3
Deep neural networks are often coupled with traditional spatial filters, such as MVDR beamformers for effectively exploiting spatial information. Even though single-stage end-to-en…
Time-domain Ad-hoc Array Speech Enhancement Using a Triple-path Network
Ashutosh Pandey, Buye Xu, Anurag Kumar +3
Deep neural networks (DNNs) are very effective for multichannel speech enhancement with fixed array geometries. However, it is not trivial to use DNNs for ad-hoc arrays with unknow…
TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement
Ashutosh Pandey, Buye Xu, Anurag Kumar +3
In this work, we propose a new model called triple-path attentive recurrent network (TPARN) for multichannel speech enhancement in the time domain. TPARN extends a single-channel d…
Localization Based Sequential Grouping for Continuous Speech Separation
Zhong-Qiu Wang, DeLiang Wang
This study investigates robust speaker localization for con-tinuous speech separation and speaker diarization, where we use speaker directions to group non-contiguous segments of t…
Self-attending RNN for Speech Enhancement to Improve Cross-corpus Generalization
Ashutosh Pandey, DeLiang Wang
Deep neural networks (DNNs) represent the mainstream methodology for supervised speech enhancement, primarily due to their capability to model complex functions using hierarchical…
Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach
Hao Zhang, DeLiang Wang
Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel A…