# CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition

arXiv: https://arxiv.org/abs/2401.02046 · HTML: https://mlsanity.com/paper/2401.02046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding](https://mlsanity.com/paper/2409.20313/) (2024)

## Abstract (arXiv)

Deploying end-to-end speech recognition models with limited computing resources remains challenging, despite their impressive performance. Given the gradual increase in model size and the wide range of model applications, selectively executing model components for different inputs to improve the inference efficiency is of great interest. In this paper, we propose a dynamic layer-skipping method that leverages the CTC blank output from intermediate layers to trigger the skipping of the last few encoder layers for frames with high blank probabilities. Furthermore, we factorize the CTC output distribution and perform knowledge distillation on intermediate layers to reduce computation and improve recognition accuracy. Experimental results show that by utilizing the CTC blank, the encoder layer depth can be adjusted dynamically, resulting in 29% acceleration of the CTC model inference with minor performance degradation.