# Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition

arXiv: https://arxiv.org/abs/2005.07903 · HTML: https://mlsanity.com/paper/2005.07903/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 24 of 24)

- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://mlsanity.com/paper/2206.08317/) (2022)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Intermediate Loss Regularization for CTC-based Speech Recognition](https://mlsanity.com/paper/2102.03216/) (2021)
- [PIMNet: A Parallel, Iterative and Mimicking Network for Scene Text Recognition](https://mlsanity.com/paper/2109.04145/) (2021)
- [A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation](https://mlsanity.com/paper/2110.05249/) (2021)
- [Revisiting Checkpoint Averaging for Neural Machine Translation](https://mlsanity.com/paper/2210.11803/) (2022)
- [IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text Recognition](https://mlsanity.com/paper/2312.11923/) (2023)
- [Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models](https://mlsanity.com/paper/2107.09428/) (2021)
- [DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents](https://mlsanity.com/paper/2407.09103/) (2024)
- [An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition](https://mlsanity.com/paper/2106.09885/) (2021)
- [TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition](https://mlsanity.com/paper/2104.01522/) (2021)
- [Decoupling recognition and transcription in Mandarin ASR](https://mlsanity.com/paper/2108.01129/) (2021)
- [Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition](https://mlsanity.com/paper/2109.06684/) (2021)
- [InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR](https://mlsanity.com/paper/2204.00174/) (2022)
- [A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition](https://mlsanity.com/paper/2304.07611/) (2023)
- [Semi-Autoregressive Streaming ASR With Label Context](https://mlsanity.com/paper/2309.10926/) (2023)
- [Transformer-based end-to-end speech recognition with residual Gaussian-based self-attention](https://mlsanity.com/paper/2103.15722/) (2021)
- [Conformer-based End-to-end Speech Recognition With Rotary Position Embedding](https://mlsanity.com/paper/2107.05907/) (2021)
- [Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy](https://mlsanity.com/paper/2303.07687/) (2023)
- [Pay Attention to CTC: Fast and Robust Pseudo-Labelling for Unified Speech Recognition](https://mlsanity.com/paper/2602.19316/) (2026)
- [Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition](https://mlsanity.com/paper/2210.14725/) (2022)
- [A Lexical-aware Non-autoregressive Transformer-based ASR Model](https://mlsanity.com/paper/2305.10839/) (2023)
- [H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems](https://mlsanity.com/paper/2508.18295/) (2025)

## Abstract (arXiv)

Non-autoregressive transformer models have achieved extremely fast inference speed and comparable performance with autoregressive sequence-to-sequence models in neural machine translation. Most of the non-autoregressive transformers decode the target sequence from a predefined-length mask sequence. If the predefined length is too long, it will cause a lot of redundant calculations. If the predefined length is shorter than the length of the target sequence, it will hurt the performance of the model. To address this problem and improve the inference speed, we propose a spike-triggered non-autoregressive transformer model for end-to-end speech recognition, which introduces a CTC module to predict the length of the target sequence and accelerate the convergence. All the experiments are conducted on a public Chinese mandarin dataset AISHELL-1. The results show that the proposed model can accurately predict the length of the target sequence and achieve a competitive performance with the advanced transformers. What's more, the model even achieves a real-time factor of 0.0056, which exceeds all mainstream speech recognition models.