Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition

Understand

Optimization of modern ASR architectures is among the highest priority tasks since it saves many computational resources for model training and inference.

• The work proposes a new Uconv-Conformer architecture based on the standard Conformer model.
• It consistently reduces the input sequence length by 16 times, which results in speeding up the work of the intermediate layers.
• To solve the convergence issue connected with such a significant reduction of the time dimension, we use upsampling blocks like in the U-Net architecture to ensure the correct CTC loss calculation and stabilize network training.