16 citations · 36 across the 14 of their papers we have counts for
5 papers · 1 filter
RTFS-Net: Recurrent Time-Frequency Modelling for Efficient Audio-Visual Speech Separation
Samuel Pegg, Kai Li, Xiaolin Hu
Audio-visual speech separation methods aim to integrate different modalities to generate high-quality separated speech, thereby enhancing the performance of downstream tasks such a…
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Kai Li, Runxuan Yang, Fuchun Sun +1
Recent research has made significant progress in designing fusion modules for audio-visual speech separation. However, they predominantly focus on multi-modal fusion at a single te…
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
Héctor Martel, Julius Richter, Kai Li +2
We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separati…
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems
Aminul Huq, Weiyi Zhang, Xiaolin Hu
Automatic speech recognition (ASR) systems based on deep neural networks are weak against adversarial perturbations. We propose mixPGD adversarial training method to improve the ro…
NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning
Jianfeng Wang, Xiaolin Hu, Thomas Lukasiewicz
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this wor…