2 citations · 6 across the 5 of their papers we have counts for
5 papers
Study of GANs for Noisy Speech Simulation from Clean Speech
Leander Melroy Maben, Zixun Guo, Chen Chen +2
The performance of speech processing models trained on clean speech drops significantly in noisy conditions. Training with noisy datasets alleviates the problem, but procuring such…
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition
Yuchen Hu, Ruizhe Li, Chen Chen +3
Audio-visual speech recognition (AVSR) research has gained a great success recently by improving the noise-robustness of audio-only automatic speech recognition (ASR) with noise-in…
Unsupervised Noise adaptation using Data Simulation
Chen Chen, Yuchen Hu, Heqing Zou +2
Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm. However, such a trained-well transformati…
Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation
Yuchen Hu, Chen Chen, Heqing Zou +2
Recent studies in neural network-based monaural speech separation (SS) have achieved a remarkable success thanks to increasing ability of long sequence modeling. However, they woul…
Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognition
Yuchen Hu, Chen Chen, Ruizhe Li +2
Speech enhancement (SE) is proved effective in reducing noise from noisy speech signals for downstream automatic speech recognition (ASR), where multi-task learning strategy is emp…