# Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT

arXiv: https://arxiv.org/abs/2102.07594 · HTML: https://mlsanity.com/paper/2102.07594/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 71 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Non-Autoregressive Neural Machine Translation](https://mlsanity.com/paper/1711.02281/) (2017)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [A Comparative Study on Transformer vs RNN in Speech Applications](https://mlsanity.com/paper/1909.06317/) (2019)
- [Scaling Neural Machine Translation](https://mlsanity.com/paper/1806.00187/) (2018)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://mlsanity.com/paper/1802.06901/) (2018)
- [Transformers without Tears: Improving the Normalization of Self-Attention](https://mlsanity.com/paper/1910.05895/) (2019)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [On Using Monolingual Corpora in Neural Machine Translation](https://mlsanity.com/paper/1503.03535/) (2015)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [Star-Transformer](https://mlsanity.com/paper/1902.09113/) (2019)
- [FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow](https://mlsanity.com/paper/1909.02480/) (2019)
- [Cold Fusion: Training Seq2Seq Models Together with Language Models](https://mlsanity.com/paper/1708.06426/) (2017)

## Built on by (most cited first, 30 of 31)

- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://mlsanity.com/paper/2206.08317/) (2022)
- [A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond](https://mlsanity.com/paper/2204.09269/) (2022)
- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers](https://mlsanity.com/paper/2202.07894/) (2022)
- [Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition](https://mlsanity.com/paper/2109.09161/) (2021)
- [Improving CTC-based speech recognition via knowledge transferring from pre-trained language models](https://mlsanity.com/paper/2203.03582/) (2022)
- [Non-autoregressive Transformer-based End-to-end ASR using BERT](https://mlsanity.com/paper/2104.04805/) (2021)
- [A context-aware knowledge transferring strategy for CTC-based ASR](https://mlsanity.com/paper/2210.06244/) (2022)
- [Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study](https://mlsanity.com/paper/2401.12789/) (2024)
- [BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model](https://mlsanity.com/paper/2210.16663/) (2022)
- [Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation](https://mlsanity.com/paper/2301.13003/) (2023)
- [An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition](https://mlsanity.com/paper/2106.09885/) (2021)
- [BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder](https://mlsanity.com/paper/2211.00792/) (2022)
- [Improving Mandarin End-to-End Speech Recognition with Word N-gram Language Model](https://mlsanity.com/paper/2201.01995/) (2022)
- [Distilling the Knowledge of BERT for CTC-based ASR](https://mlsanity.com/paper/2209.02030/) (2022)
- [Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM](https://mlsanity.com/paper/2209.04062/) (2022)
- [Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers](https://mlsanity.com/paper/2209.12816/) (2022)
- [A CTC Alignment-based Non-autoregressive Transformer for End-to-end Automatic Speech Recognition](https://mlsanity.com/paper/2304.07611/) (2023)
- [Keep Decoding Parallel with Effective Knowledge Distillation from Language Models to End-to-end Speech Recognisers](https://mlsanity.com/paper/2401.11700/) (2024)
- [Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR](https://mlsanity.com/paper/2409.02239/) (2024)
- [Multiple Representation Transfer from Large Language Models to End-to-End ASR Systems](https://mlsanity.com/paper/2309.04031/) (2023)
- [Unimodal Aggregation for CTC-based Speech Recognition](https://mlsanity.com/paper/2309.08150/) (2023)
- [UniEnc-CASSNAT: An Encoder-only Non-autoregressive ASR for Speech SSL Models](https://mlsanity.com/paper/2402.08898/) (2024)
- [Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR](https://mlsanity.com/paper/2505.13079/) (2025)
- [EvoText: Enhancing Natural Language Generation Models via Self-Escalation Learning for Up-to-Date Knowledge and Improved Performance](https://mlsanity.com/paper/2302.03896/) (2023)
- [A Lexical-aware Non-autoregressive Transformer-based ASR Model](https://mlsanity.com/paper/2305.10839/) (2023)
- [SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge](https://mlsanity.com/paper/2309.01437/) (2023)
- [Predictability and Causality in Spanish and English Natural Language Generation](https://mlsanity.com/paper/2408.14283/) (2024)
- [New Insights into Optimal Alignment of Acoustic and Linguistic Representations for Knowledge Transfer in ASR](https://mlsanity.com/paper/2509.05609/) (2025)

## Abstract (arXiv)

Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because the decoder predicts text tokens (such as characters or words) in an autoregressive manner, it is difficult for an AED model to predict all tokens in parallel. This makes the inference speed relatively slow. We believe that because the encoder already captures the whole speech utterance, which has the token-level relationship implicitly, we can predict a token without explicitly autoregressive language modeling. When the prediction of a token does not rely on other tokens, the parallel prediction of all tokens in the sequence is realizable. Based on this idea, we propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once). The model consists of an encoder, a decoder, and a position dependent summarizer (PDS). The three modules are based on basic attention blocks. The encoder extracts high-level representations from the speech. The PDS uses positional encodings corresponding to tokens to convert the acoustic representations into token-level representations. The decoder further captures token-level relationships with the self-attention mechanism. At last, the probability distribution on the vocabulary is computed for each token position. Therefore, speech recognition is re-formulated as a position-wise classification problem. Further, we propose a cross-modal transfer learning method to refine semantics from a large-scale pre-trained language model BERT for improving the performance.