  Audio-visual speech enhancement system is regarded to be one of promising
solutions for isolating and enhancing speech of desired speaker. Conventional
methods focus on predicting clean speech spectrum via a naive convolution
neural network based encoder-decoder architecture, and these methods a) not
adequate to use data fully and effectively, b) cannot process features
selectively. The proposed model addresses these drawbacks, by a) applying a
model that fuses audio and visual features layer by layer in encoding phase,
and that feeds fused audio-visual features to each corresponding decoder layer,
and more importantly, b) introducing soft threshold attention into the model to
select the informative modality softly. This paper proposes attentional
audio-visual multi-layer feature fusion model, in which soft threshold
attention unit are applied on feature mapping at every layer of decoder. The
proposed model demonstrates the superior performance of the network against the
state-of-the-art models.
