Watch to Listen Clearly: Visual Speech Enhancement Driven Multi-modality Speech Recognition

Multi-modality (talking face video and audio) information helps improve speech recognition performance compared to the single modality. In noisy environments, the effect of audio modality is weakened, which further affects the performance of multi-modality speech recognition (MSR). Most of the MSR methods use noisy audio signal as input of the audio modality without any enhancement (filtering the noisy components in the audio signal). In this paper, we propose an audio-enhanced multi-modality speech recognition model. In particular, the proposed model consists of two sub-networks, one is the visual speech enhancement (VE) sub-network and the other is the multi-modality speech recognition (MSR) sub-network. The VE sub-network is able to separate a speaker's voice from background noises when given the corresponding talking face to enhance audio modality. Then the audio modality together with video modality are fed into the MSR sub-network to produce characters. We introduce a pseudo-3D residual network (P3D)-based visual front-end to extract more advantageous visual features. The MSR sub-network is built on top of the Element-wise-Attention Gated Recurrent Unit (EleAtt-GRU) architecture which is more effective than Transformer in long sequences. We demonstrate the effectiveness of audio enhancement for MSR by extensive experiments. The proposed method surpasses the state-of-the-art MSR models on the LRS3-TED dataset and the LRW dataset.