2 citations · 5 across the 7 of their papers we have counts for
4 papers · 1 filter
Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation
Rohith Aralikatti, Zhenyu Tang, Dinesh Manocha
We present a novel approach to improve the performance of learning-based speech dereverberation using accurate synthetic datasets. Our approach is designed to recover the reverb-fr…
Online Self-Attentive Gated RNNs for Real-Time Speaker Separation
Ori Kabeli, Yossi Adi, Zhenyu Tang +2
Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produ…
Improving Reverberant Speech Separation with Multi-stage Training and Curriculum Learning
Rohith Aralikatti, Anton Ratnarajah, Zhenyu Tang +1
We present a novel approach that improves the performance of reverberant speech separation. Our approach is based on an accurate geometric acoustic simulator (GAS) which generates…
Scene-aware Far-field Automatic Speech Recognition
Zhenyu Tang, Dinesh Manocha
We propose a novel method for generating scene-aware training data for far-field automatic speech recognition. We use a deep learning-based estimator to non-intrusively compute the…