Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses
Abstract
This paper introduces a new paradigm for generative error correction (GER) framework in audio-visual speech recognition (AVSR) that reasons over modality-specific evidences directly in the language space. Our framework, DualHyp, empowers a large language model (LLM) to compose independent N-best hypotheses from separate automatic speech recognition (ASR) and visual speech recognition (VSR) models. To maximize the effectiveness of DualHyp, we further introduce RelPrompt, a noise-aware guidance mechanism that provides modality-grounded prompts to the LLM. RelPrompt offers the temporal reliability of each modality stream, guiding the model to dynamically switch its focus between ASR and VSR hypotheses for an accurate correction. Under various corruption scenarios, our framework attains up to 57.7% error rate gain on the LRS2 benchmark over standard ASR baseline, contrary to single-stream GER approaches that achieve only 10% gain. To facilitate research within our DualHyp framework, we release the code and the dataset comprising ASR and VSR hypotheses at https://github.com/sungnyun/dualhyp.
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 the DualHyp framework for audio-visual speech error correction, which significantly outperforms traditional single-stream approaches. This work represents a meaningful advancement in the field of audio-visual speech recognition, combining innovative methodologies with practical implications for future research and applications.
Methodology
The proposed DualHyp framework innovatively integrates modality-specific evidence from both ASR and VSR systems to enhance error correction in audio-visual speech recognition. By leveraging a large language model to generate independent hypotheses and employing the RelPrompt mechanism for noise-aware guidance, the methodology showcases a thoughtful approach to addressing the challenges of modality dependency and error correction. However, the complexity of the architecture and the reliance on high-quality upstream models may limit its practical applicability.
Results & evaluation
The experiments conducted on the LRS2 benchmark demonstrate a significant improvement in error rates compared to standard ASR baselines, achieving a 57.7% error rate gain. The evaluation under various corruption scenarios provides a robust assessment of the framework's effectiveness. However, the paper could benefit from a more detailed analysis of the experimental setup, including the specific metrics used and comparisons with other state-of-the-art methods.
Reproducibility
The authors have made the code and dataset publicly available, which is a positive step towards reproducibility. However, the paper lacks detailed implementation instructions and hyperparameter settings, which may hinder other researchers from replicating the results easily.
Limitations
The paper acknowledges two primary limitations: the dependency on the quality of the upstream speech recognition models and the computational latency introduced by the multi-module structure. These limitations highlight challenges in real-time applications and adaptation to multilingual contexts, which could restrict the framework's broader applicability.
Broader impact
The proposed framework has significant potential applications in improving speech recognition systems, particularly in scenarios where audio and visual cues can be leveraged for better accuracy. Its implications extend to assistive technologies, communication aids, and enhancing user experiences in various interactive systems. However, the identified limitations may pose challenges in scaling the approach to diverse languages and real-time environments. The main contribution of this paper is the introduction of the DualHyp framework for audio-visual speech error correction, which significantly outperforms traditional single-stream approaches. This work represents a meaningful advancement in the field of audio-visual speech recognition, combining innovative methodologies with practical implications for future research and applications.
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