9 citations · 25 across the 12 of their papers we have counts for
6 papers · 1 filter
MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition
Yuchen Hu, Chen Chen, Ruizhe Li +2
Audio-visual speech recognition (AVSR) attracts a surge of research interest recently by leveraging multimodal signals to understand human speech. Mainstream approaches addressing…
Towards Balanced Active Learning for Multimodal Classification
Meng Shen, Yizheng Huang, Jianxiong Yin +3
Training multimodal networks requires a vast amount of data due to their larger parameter space compared to unimodal networks. Active learning is a widely used technique for reduci…
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning
Heqing Zou, Meng Shen, Chen Chen +3
Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks. However, traditional aggregation-based…
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…