28 citations · 164 across the 30 of their papers we have counts for
8 papers · 1 filter
3M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognition
Zhao You, Shulin Feng, Dan Su +1
Recently, Conformer based CTC/AED model has become a mainstream architecture for ASR. In this paper, based on our prior work, we identify and integrate several approaches to achiev…
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition
Max W. Y. Lam, Jun Wang, Chao Weng +2
End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization. To extract learnable…
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts
Zhao You, Shulin Feng, Dan Su +1
Recently, Mixture of Experts (MoE) based Transformer has shown promising results in many domains. This is largely due to the following advantages of this architecture: firstly, MoE…
Complex Neural Spatial Filter: Enhancing Multi-channel Target Speech Separation in Complex Domain
Rongzhi Gu, Shi-Xiong Zhang, Yuexian Zou +1
To date, mainstream target speech separation (TSS) approaches are formulated to estimate the complex ratio mask (cRM) of the target speech in time-frequency domain under supervised…
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation
Xiyun Li, Yong Xu, Meng Yu +4
Recently, our proposed recurrent neural network (RNN) based all deep learning minimum variance distortionless response (ADL-MVDR) beamformer method yielded superior performance ove…
Generalized Spatio-Temporal RNN Beamformer for Target Speech Separation
Yong Xu, Zhuohuang Zhang, Meng Yu +2
Although the conventional mask-based minimum variance distortionless response (MVDR) could reduce the non-linear distortion, the residual noise level of the MVDR separated speech i…