MIR-GAN: Refining Frame-Level Modality-Invariant Representations with
  Adversarial Network for Audio-Visual Speech Recognition
Yuchen Hu Chen Chen Ruizhe Li Heqing Zou Eng Siong Chng
Audio-visual speech recognition (AVSR) attracts a surge of research interest
recently by leveraging multimodal signals to understand human speech.
Mainstream approaches addressing this task have developed sophisticated
architectures and techniques for multi-modality fusion and representation
learning. However, the natural heterogeneity of different modalities causes
distribution gap between their representations, making it challenging to fuse
them. In this paper, we aim to learn the shared representations across
modalities to bridge their gap. Different from existing similar methods on
other multimodal tasks like sentiment analysis, we focus on the temporal
contextual dependencies considering the sequence-to-sequence task setting of
AVSR. In particular, we propose an adversarial network to refine frame-level
modality-invariant representations (MIR-GAN), which captures the commonality
across modalities to ease the subsequent multimodal fusion process. Extensive
experiments on public benchmarks LRS3 and LRS2 show that our approach
outperforms the state-of-the-arts.