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