arXiv:2403.04245v1 Announce Type: cross
Abstract: Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames, performing even worse than single-modality models. While applying the dropout technique to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. In this paper, we investigate this contrasting phenomenon from the perspective of modality bias and reveal that an excessive modality bias on the audio caused by dropout is the underlying reason. Moreover, we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between modality bias and robustness against missing modality in multimodal systems. Building on these findings, we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality and to maintain performance and robustness simultaneously. Finally, to address an entirely missing modality, we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated and validated through a series of comprehensive experiments using the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR
Analyzing the Modality Bias in Advanced Audio-Visual Speech Recognition
Advanced Audio-Visual Speech Recognition (AVSR) systems have shown great potential in improving the accuracy and robustness of speech recognition by utilizing both audio and visual modalities. However, recent studies have observed that AVSR systems can be sensitive to missing video frames, performing even worse than single-modality models. This raises the need for a deeper understanding of the underlying reasons and potential solutions to overcome this limitation.
In this paper, the authors delve into the issue of modality bias and its impact on AVSR systems. Specifically, they investigate the contrasting phenomenon where applying the dropout technique to the video modality enhances robustness to missing frames, yet results in performance loss with complete data input. Through their analysis, they identify that an excessive modality bias on the audio caused by dropout is the root cause of this issue.
The authors propose the Modality Bias Hypothesis (MBH) to systematically describe the relationship between modality bias and robustness against missing modality in multimodal systems. This hypothesis sheds light on the fact that the dropout technique, while beneficial in certain scenarios, can create an imbalance between the audio and visual modalities, leading to suboptimal performance.
Building upon their findings, the authors present a novel solution called the Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework. This framework aims to reduce the over-reliance on the audio modality and maintain performance and robustness simultaneously. By addressing the modality bias issue, the MDA-KD framework enhances the overall effectiveness of AVSR systems.
Additionally, the authors acknowledge the possibility of an entirely missing modality and propose the use of adapters to dynamically switch decision strategies. This adaptive approach ensures that AVSR systems can handle cases where one of the modalities is completely unavailable.
The content of this paper is highly relevant to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. AVSR systems are integral components of various multimedia applications, such as virtual reality environments and augmented reality applications, where accurate and robust speech recognition is crucial for user interaction. By examining the modality bias issue, this paper contributes to the development of more effective and reliable AVSR systems, thus enhancing the overall user experience and immersion in multimedia environments.
To summarize, this paper provides an insightful analysis of the modality bias in AVSR systems and its impact on the robustness of speech recognition. The proposed Modality Bias Hypothesis and the MDA-KD framework offer a promising path towards mitigating this issue and improving the performance of multimodal systems. By addressing this challenge, the paper contributes to the advancement of multimedia information systems and related disciplines, fostering the development of more immersive and interactive multimedia experiences.