Cross-Modal Global Interaction and Local Alignment for Audio-Visual
  Speech Recognition
Yuchen Hu Ruizhe Li Chen Chen Heqing Zou Qiushi Zhu Eng Siong Chng
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-invariant visual information. However, most
existing AVSR approaches simply fuse the audio and visual features by
concatenation, without explicit interactions to capture the deep correlations
between them, which results in sub-optimal multimodal representations for
downstream speech recognition task. In this paper, we propose a cross-modal
global interaction and local alignment (GILA) approach for AVSR, which captures
the deep audio-visual (A-V) correlations from both global and local
perspectives. Specifically, we design a global interaction model to capture the
A-V complementary relationship on modality level, as well as a local alignment
approach to model the A-V temporal consistency on frame level. Such a holistic
view of cross-modal correlations enable better multimodal representations for
AVSR. Experiments on public benchmarks LRS3 and LRS2 show that our GILA
outperforms the supervised learning state-of-the-art.