Robust LLM-based Audio-Visual Speech Recognition with Sparse Modality Alignment and Visual Unit-Guided Refinement
Abstract
Audio-Visual Speech Recognition (AVSR) integrates acoustic and visual information to enhance robustness in adverse acoustic conditions. Recent advances in Large Language Models (LLMs) have yielded competitive automatic speech recognition performance and shown effectiveness for AVSR. However, prior approaches project audio and visual features independently or apply shallow fusion, limiting cross-modal alignment and complementary exchange while increasing the LLM's computational load. To address this, we propose AVUR-LLM, an LLM-based Audio-Visual Speech Recognition via Sparse Modality Alignment and Visual Unit-Guided Refinement. Experiments on LRS3 demonstrate state-of-the-art results for AVSR. Under additive-noise conditions at 0 dB SNR, it achieves 37% relative improvement over the baseline system.
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 AVUR-LLM, a novel framework for audio-visual speech recognition that leverages sparse modality alignment and visual unit-guided refinement to achieve state-of-the-art performance in challenging acoustic conditions. This work significantly advances the field of AVSR by addressing key limitations of existing methods and demonstrating the potential for improved robustness and accuracy in speech recognition tasks.
Methodology
The proposed methodology introduces several innovative components such as Sparse Modality Alignment (SMA), Adaptive Modulated Fusion (AMF), and Visual Unit-Guided Refinement (VUR). SMA allows for a more controlled interaction between audio and visual modalities by inserting alignment blocks into the audio encoder, which is a significant improvement over existing methods that typically rely on shallow fusion. The AMF component intelligently modulates visual feature injection based on acoustic reliability, enhancing the model's adaptability to varying input conditions. The VUR approach effectively transforms visual representations into discrete tokens for LLM rescoring, which is a novel strategy that leverages the strengths of both visual and language models. Overall, the methodology is well-structured and addresses key limitations in prior AVSR systems.
Results & evaluation
The experiments conducted on the LRS3 dataset demonstrate the effectiveness of the proposed model, achieving state-of-the-art results in various noise conditions. The reported 37% relative improvement in Word Error Rate (WER) under 0 dB SNR conditions is particularly noteworthy, showcasing the robustness of the model in challenging scenarios. The ablation studies provide additional insights into the contributions of each component, reinforcing the validity of the proposed framework. However, the paper could benefit from a more detailed discussion on the statistical significance of the results and comparisons with a broader range of existing methods.
Reproducibility
The paper provides a comprehensive overview of the experimental setup, including details on the dataset, model architecture, training procedures, and evaluation metrics. However, the lack of a publicly available code repository or demo URL limits the reproducibility of the results. Future work should consider making the implementation accessible to facilitate validation by the research community.
Limitations
One limitation of the study is the reliance on a single dataset (LRS3) for evaluation, which may not fully capture the generalizability of the model across different domains or languages. Additionally, while the method shows improvements in noise robustness, the paper does not explore the performance in extremely adverse conditions or with diverse accents and speech patterns. The computational efficiency of the proposed model, particularly in real-time applications, is also not thoroughly addressed.
Broader impact
The advancements in AVSR presented in this paper have significant implications for various applications, including assistive technologies for the hearing impaired, video conferencing systems, and automated transcription services. By enhancing the robustness of speech recognition in noisy environments, this research contributes to making communication technologies more accessible and effective. The main contribution of this paper is the introduction of AVUR-LLM, a novel framework for audio-visual speech recognition that leverages sparse modality alignment and visual unit-guided refinement to achieve state-of-the-art performance in challenging acoustic conditions. This work significantly advances the field of AVSR by addressing key limitations of existing methods and demonstrating the potential for improved robustness and accuracy in speech recognition tasks.
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