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Audio-visual speech recognition (AVSR) system is thought to be one of themost promising solutions for robust speech recognition, especially in noisyenvironment. In this paper, we propose a novel multimodal attention basedmethod for audio-visual speech recognition which could automatically learn thefused representation from both modalities based on their importance. Our methodis realized using state-of-the-art sequence-to-sequence (Seq2seq)architectures. Experimental results show that relative improvements from 2% upto 36% over the auditory modality alone are obtained depending on the differentsignal-to-noise-ratio (SNR). Compared to the traditional feature concatenationmethods, our proposed approach can achieve better recognition performance underboth clean and noisy conditions. We believe modality attention based end-to-endmethod can be easily generalized to other multimodal tasks with correlatedinformation.

Modality Attention for End-to-End Audio-visual Speech Recognition