# BART based semantic correction for Mandarin automatic speech recognition system

arXiv: https://arxiv.org/abs/2104.05507 · HTML: https://mlsanity.com/paper/2104.05507/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Pre-trained Models for Natural Language Processing: A Survey](https://mlsanity.com/paper/2003.08271/) (2020)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model](https://mlsanity.com/paper/1910.10697/) (2019)
- [ASR Error Correction and Domain Adaptation Using Machine Translation](https://mlsanity.com/paper/2003.07692/) (2020)
- [THCHS-30 : A Free Chinese Speech Corpus](https://mlsanity.com/paper/1512.01882/) (2015)
- [Joint Contextual Modeling for ASR Correction and Language Understanding](https://mlsanity.com/paper/2002.00750/) (2020)
- [A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition](https://mlsanity.com/paper/1810.11352/) (2018)

## Built on by (most cited first, 20 of 20)

- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space](https://mlsanity.com/paper/2303.00456/) (2023)
- [HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models](https://mlsanity.com/paper/2309.15701/) (2023)
- [ASR Error Correction using Large Language Models](https://mlsanity.com/paper/2409.09554/) (2024)
- [ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction](https://mlsanity.com/paper/2310.05129/) (2023)
- [ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction](https://mlsanity.com/paper/2412.03075/) (2024)
- [ASR Error Correction with Constrained Decoding on Operation Prediction](https://mlsanity.com/paper/2208.04641/) (2022)
- [Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM](https://mlsanity.com/paper/2209.04062/) (2022)
- [Unsupervised domain adaptation for speech recognition with unsupervised error correction](https://mlsanity.com/paper/2209.12043/) (2022)
- [Revisiting ASR Error Correction with Specialized Models](https://mlsanity.com/paper/2405.15216/) (2024)
- [Adapting an Unadaptable ASR System](https://mlsanity.com/paper/2306.01208/) (2023)
- [Robust ASR Error Correction with Conservative Data Filtering](https://mlsanity.com/paper/2407.13300/) (2024)
- [MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical Speech-to-Formula](https://mlsanity.com/paper/2412.15655/) (2024)
- [CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese](https://mlsanity.com/paper/2511.04139/) (2025)
- [Enhancing conversational quality in language learning chatbots: An evaluation of GPT4 for ASR error correction](https://mlsanity.com/paper/2307.09744/) (2023)
- [Multiple Representation Transfer from Large Language Models to End-to-End ASR Systems](https://mlsanity.com/paper/2309.04031/) (2023)
- [Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting](https://mlsanity.com/paper/2310.13321/) (2023)
- [HypR: A comprehensive study for ASR hypothesis revising with a reference corpus](https://mlsanity.com/paper/2309.09838/) (2023)
- [PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding](https://mlsanity.com/paper/2506.11064/) (2025)
- [Listen, Attend, Understand: a Regularization Technique for Stable E2E Speech Translation Training on High Variance labels](https://mlsanity.com/paper/2601.01121/) (2026)

## Abstract (arXiv)

Although automatic speech recognition (ASR) systems achieved significantly improvements in recent years, spoken language recognition error occurs which can be easily spotted by human beings. Various language modeling techniques have been developed on post recognition tasks like semantic correction. In this paper, we propose a Transformer based semantic correction method with pretrained BART initialization, Experiments on 10000 hours Mandarin speech dataset show that character error rate (CER) can be effectively reduced by 21.7% relatively compared to our baseline ASR system. Expert evaluation demonstrates that actual improvement of our model surpasses what CER indicates.