Seeing the Context: Rich Visual Context-Aware Speech Recognition via Multimodal Reasoning
Abstract
Audio-visual speech recognition (AVSR) is an extension of ASR that incorporates visual signals. Current AVSR approaches primarily focus on lip motion, largely overlooking rich context present in the video such as speaking scene and on-screen text. To tackle such CAVSR (AVSR including rich visual Context), we propose VASR designed to "see" and reason the visual context to improve speech recognition. Specifically, we construct an Audio-Visual Chain-of-Thought (AV-CoT) that explicitly enforces intermediate cross-modal grounding between acoustic signals and visual evidence. This evidence-driven reasoning mitigates the "single-modality dominance" problem, where models either over-rely on visual context or fail to utilize it. Besides, to address the data scarcity, we construct and release a corresponding data pipeline and test set. Experiments show that AV-CoT effectively mitigates the single-modality dominance, achieving state-of-the-art performance in CAVSR. The project is open-sourced.
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 paper presents a novel approach to context-aware audio-visual speech recognition by leveraging rich visual context through a structured reasoning framework. This work significantly advances the field by addressing the limitations of existing AVSR methods and providing a comprehensive dataset for future research.
Methodology
The proposed methodology introduces the Audio-Visual Chain-of-Thought (AV-CoT) framework, which is a structured approach to integrate visual context into speech recognition tasks. This is a significant advancement over traditional AVSR methods that primarily focus on lip movements. The three-step process of Perception, Reasoning, and Transcription is well-defined, allowing for a systematic approach to disambiguate speech using multimodal inputs. The authors also address the challenge of data scarcity by developing a scalable data pipeline, which is a commendable effort in enhancing the dataset quality for CAVSR tasks.
Results & evaluation
The experiments are thorough, demonstrating the effectiveness of the VASR model against several strong baselines. The use of character error rate (CER) as a metric is appropriate for the task, and the results indicate a significant performance improvement over existing models. The ablation studies provide additional insights into the importance of the AV-CoT mechanism, reinforcing the claims made about its effectiveness in mitigating single-modality dominance.
Reproducibility
The authors provide sufficient implementation details, including the model architecture, training parameters, and data processing pipeline. However, the reproducibility could be enhanced by providing more detailed descriptions of the datasets used and ensuring that all code and data are readily accessible for independent verification.
Limitations
One notable limitation is the reliance on the Qwen2.5-Omni model, which has a low frame rate for visual encoding, potentially impacting the performance of the lip-reading task. Additionally, the paper does not address the potential biases that may arise from the datasets used, which could affect the generalizability of the results.
Broader impact
The research has significant implications for improving speech recognition systems, particularly in contexts where visual cues are abundant. This could enhance accessibility for individuals with hearing impairments and improve user experience in various multimedia applications. The open-sourcing of the dataset and code also promotes further research in this area. The paper presents a novel approach to context-aware audio-visual speech recognition by leveraging rich visual context through a structured reasoning framework. This work significantly advances the field by addressing the limitations of existing AVSR methods and providing a comprehensive dataset for future research.
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