Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
Abstract
Audio-Visual Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains unclear. We present Dr. SHAP-AV, a framework using Shapley values to analyze modality contributions in AVSR. Through experiments on six models across two benchmarks and varying SNR levels, we introduce three analyses: Global SHAP for overall modality balance, Generative SHAP for contribution dynamics during decoding, and Temporal Alignment SHAP for input-output correspondence. Our findings reveal that models shift toward visual reliance under noise yet maintain high audio contributions even under severe degradation. Modality balance evolves during generation, temporal alignment holds under noise, and SNR is the dominant factor driving modality weighting. These findings expose a persistent audio bias, motivating ad-hoc modality-weighting mechanisms and Shapley-based attribution as a standard AVSR diagnostic.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of Dr. SHAP-AV, a framework that utilizes Shapley values to analyze and decode the contributions of audio and visual modalities in speech recognition under varying noise conditions. This work significantly advances the understanding of modality interactions in AVSR, providing a foundation for future research and practical applications in the field.
Methodology
The paper introduces Dr. SHAP-AV, a framework that utilizes Shapley values to analyze the contributions of audio and visual modalities in AVSR. The methodology is well-structured, with three distinct analyses (Global SHAP, Generative SHAP, and Temporal Alignment SHAP) that provide a comprehensive understanding of modality contributions. The use of Shapley values is innovative in this context, as it allows for a nuanced exploration of how models adapt their reliance on audio and visual inputs under varying noise conditions. The framework is theoretically sound and leverages established concepts in game theory to address a practical problem in AVSR.
Results & evaluation
The experiments conducted on six models across two benchmarks and varying signal-to-noise ratios (SNR) are robust and provide valuable insights into the dynamics of modality contributions. The results demonstrate a clear shift towards visual reliance in noisy environments while maintaining audio contributions, which is a significant finding. The experiments are well-designed, and the analysis of results is thorough, providing a solid basis for the conclusions drawn.
Reproducibility
The paper lacks detailed implementation specifics that would facilitate reproducibility, such as hyperparameters, data preprocessing steps, and model architectures. While the project website may provide additional resources, the absence of a code repository or detailed methodological appendices limits the ability of other researchers to replicate the findings.
Limitations
One limitation is the potential bias towards audio contributions that the authors identify, which may not be fully addressed within the framework. Additionally, the reliance on specific benchmarks may limit the generalizability of the findings to other AVSR tasks or datasets. The paper could also benefit from a broader exploration of how these findings could influence future AVSR model designs.
Broader impact
The findings have significant implications for the development of more robust AVSR systems, particularly in real-world applications where noise is prevalent. By highlighting the modality contributions and their dynamics, this research can inform the design of models that better balance audio and visual inputs, potentially leading to improved performance in challenging environments. The main contribution of this paper is the introduction of Dr. SHAP-AV, a framework that utilizes Shapley values to analyze and decode the contributions of audio and visual modalities in speech recognition under varying noise conditions. This work significantly advances the understanding of modality interactions in AVSR, providing a foundation for future research and practical applications in the field.
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