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 systems in noisy environments. We propose LipGER (Lip Motion aided Generative Error Correction)%2C a novel framework for leveraging visual cues for noise-robust ASR. Instead of learning the cross-modal correlation between the audio and visual modalities%2C we make an LLM learn the task of visually-conditioned (generative) ASR error correction. Specifically%2C we instruct an LLM to predict the transcription from the N-best hypotheses generated using ASR beam-search. This is further conditioned on lip motions. This approach addresses key challenges in traditional AVSR learning%2C such as the lack of large-scale paired datasets and difficulties in adapting to new domains. We experiment on 4 datasets in various settings and show that LipGER improves the Word Error Rate in the range of 1.1%25-49.2%25. We also release LipHyp%2C a large-scale dataset with hypothesis-transcription pairs that is additionally equipped with lip motion cues to promote further research in this space?quality=80&w=800)
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