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In this study, we try to address the problem of leveraging visual signals toimprove Automatic Speech Recognition (ASR), also known as visual context-awareASR (VC-ASR). We explore novel VC-ASR approaches to leverage video and textrepresentations extracted by a self-supervised pre-trained text-video embeddingmodel. Firstly, we propose a multi-stream attention architecture to leveragesignals from both audio and video modalities. This architecture consists ofseparate encoders for the two modalities and a single decoder that attends overthem. We show that this architecture is better than fusing modalities at thesignal level. Additionally, we also explore leveraging the visual informationin a second pass model, which has also been referred to as a `deliberationmodel'. The deliberation model accepts audio representations and texthypotheses from the first pass ASR and combines them with a visual stream foran improved visual context-aware recognition. The proposed deliberation schemecan work on top of any well trained ASR and also enabled us to leverage thepre-trained text model to ground the hypotheses with the visual features. Ourexperiments on HOW2 dataset show that multi-stream and deliberationarchitectures are very effective at the VC-ASR task. We evaluate the proposedmodels for two scenarios; clean audio stream and distorted audio in which wemask out some specific words in the audio. The deliberation model outperformsthe multi-stream model and achieves a relative WER improvement of 6% and 8.7%for the clean and masked data, respectively, compared to an audio-only model.The deliberation model also improves recovering the masked words by 59%relative.

Listen, Look and Deliberate: Visual context-aware speech recognition using pre-trained text-video representations