MIR - GAN : Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition (2023)

Abstract: 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.

## introduction human perception of the world intrinsically comprises multiple modalities, including @xcite . audio-visual speech recognition (avsr) leverages both audio and visual modalities to understand human speech, improving the noise-robustness of audio-only speech recognition with noise-invariant lip movement information @xcite . thanks to recent advances of deep learning techniques, avsr research has gained a remarkable progress @xcite @xcite . currently, the mainstream avsr approaches are centered around developing sophisticated architectures and techniques for multi-modality fusion, 1 code is available at https://github.com/yuche n005/mir-gan . ## conclusion in this paper, we propose mir-gan, an adversarial network to refine frame-level modality-invariant representations for avsr, which captures the commonality across modalities to ease the multimodal fusion process. mir-gan first learns modalityinvariant representation with mir generator, followed by a modality discriminator to strengthen its modality agnosticism via adversarial learning. furthermore, we propose a mutual information maximization strategy to enrich its contextual semantic information. finally, both modality-invariant and -specific representations are fused to provide a holistic view of multimodal data for downstream task. experiments on public benchmarks show that our mir-gan achieves the state-of-the-art.