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Audio and visual signals complement each other in human speech perception, sodo they in speech recognition. The visual hint is less evident than theacoustic hint, but more robust in a complex acoustic environment, as far asspeech perception is concerned. It remains a challenge how we effectivelyexploit the interaction between audio and visual signals for automatic speechrecognition. There have been studies to exploit visual signals as redundant orcomplementary information to audio input in a synchronous manner. Human studiessuggest that visual signal primes the listener in advance as to when and onwhich frequency to attend to. We propose a Predict-and-Update Network (P&Unet), to simulate such a visual cueing mechanism for Audio-Visual SpeechRecognition (AVSR). In particular, we first predict the character posteriors ofthe spoken words, i.e. the visual embedding, based on the visual signals. Theaudio signal is then conditioned on the visual embedding via a novelcross-modal Conformer, that updates the character posteriors. We validate theeffectiveness of the visual cueing mechanism through extensive experiments. Theproposed P&U net outperforms the state-of-the-art AVSR methods on both LRS2-BBCand LRS3-BBC datasets, with the relative reduced Word Error Rate (WER)sexceeding 10% and 40% under clean and noisy conditions, respectively.

Predict-and-Update Network: Audio-Visual Speech Recognition Inspired by Human Speech Perception