Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition

Understand

The Transformer architecture model, based on self-attention and multi-head attention, has achieved remarkable success in offline end-to-end Automatic Speech Recognition (ASR).

• However, self-attention and multi-head attention cannot be easily applied for streaming or online ASR.
• For self-attention in Transformer ASR, the softmax normalization function-based attention mechanism makes it impossible to highlight important speech information.
• For multi-head attention in Transformer ASR, it is not easy to model monotonic alignments in different heads.