Jul 06, 2022 · Conformer has proven to be effective in many speech processing tasks. It combines the benefits of extracting local dependencies using convolutions and global dependencies using self-attention. Inspired by this, we propose a more flexible, interpretable and customizable encoder alternative, Branchformer, with parallel branches for modeling various ranged dependencies in end-to-end speech .... "/>
The attention module consists of a simple 2D-convolutional layer, MLP (in the case of channel attention), and sigmoid function at the end to generate a mask of the input feature map. Fig. 1...
To extract more discriminative features, the proposed method employs a Spatial Stream and a Temporal Stream to extract the spatial and temporal features, respectively.
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Transformers have sprung up in the field of computer vision. In this work, we explore whether the core self-attention module in Transformer is the key to achieving excellent
Proposed a simple and lightweight cognitive model for smart detection systems based on speech emotions. Utilized dilated convolutional layers and introduced a two-stream self-attention module for classification problems. Utilized two-channels in attention mechanism, which recognize global cues using MLP and spatial cues using special dilated CNN.