# ASR Error Correction and Domain Adaptation Using Machine Translation

arXiv: https://arxiv.org/abs/2003.07692 · HTML: https://mlsanity.com/paper/2003.07692/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 30 of 48)

- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models](https://mlsanity.com/paper/2309.15701/) (2023)
- [FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition](https://mlsanity.com/paper/2109.14420/) (2021)
- [ASR Error Correction using Large Language Models](https://mlsanity.com/paper/2409.09554/) (2024)
- [BART based semantic correction for Mandarin automatic speech recognition system](https://mlsanity.com/paper/2104.05507/) (2021)
- [Resilience of Large Language Models for Noisy Instructions](https://mlsanity.com/paper/2404.09754/) (2024)
- [Error Correction in ASR using Sequence-to-Sequence Models](https://mlsanity.com/paper/2202.01157/) (2022)
- [Sequence-to-Action: Grammatical Error Correction with Action Guided Sequence Generation](https://mlsanity.com/paper/2205.10884/) (2022)
- [Hallucination of speech recognition errors with sequence to sequence learning](https://mlsanity.com/paper/2103.12258/) (2021)
- [Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction](https://mlsanity.com/paper/2211.13252/) (2022)
- [SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition](https://mlsanity.com/paper/2212.01039/) (2022)
- [Crossmodal ASR Error Correction with Discrete Speech Units](https://mlsanity.com/paper/2405.16677/) (2024)
- [RED-ACE: Robust Error Detection for ASR using Confidence Embeddings](https://mlsanity.com/paper/2203.07172/) (2022)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction](https://mlsanity.com/paper/2505.24347/) (2025)
- [Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection](https://mlsanity.com/paper/2201.03313/) (2022)
- [Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM](https://mlsanity.com/paper/2209.04062/) (2022)
- [ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding](https://mlsanity.com/paper/2311.11375/) (2023)
- [Unsupervised domain adaptation for speech recognition with unsupervised error correction](https://mlsanity.com/paper/2209.12043/) (2022)
- [Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries](https://mlsanity.com/paper/2105.09930/) (2021)
- [Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding](https://mlsanity.com/paper/2205.00693/) (2022)
- [Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline](https://mlsanity.com/paper/2401.14625/) (2024)
- [DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition](https://mlsanity.com/paper/2403.17645/) (2024)
- [LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors](https://mlsanity.com/paper/2505.11352/) (2025)
- [Better Transcription of UK Supreme Court Hearings](https://mlsanity.com/paper/2211.17094/) (2022)
- [PATCorrect: Non-autoregressive Phoneme-augmented Transformer for ASR Error Correction](https://mlsanity.com/paper/2302.05040/) (2023)
- [Using Text Injection to Improve Recognition of Personal Identifiers in Speech](https://mlsanity.com/paper/2308.07393/) (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)
- [A Dialogue-based Information Extraction System for Medical Insurance Assessment](https://mlsanity.com/paper/2107.05866/) (2021)

## Abstract (arXiv)

Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly viable service for companies of any size building speech-based products. While these ASR systems are trained on large amounts of data, domain mismatch is still an issue for many such parties that want to use this service as-is leading to not so optimal results for their task. We propose a simple technique to perform domain adaptation for ASR error correction via machine translation. The machine translation model is a strong candidate to learn a mapping from out-of-domain ASR errors to in-domain terms in the corresponding reference files. We use two off-the-shelf ASR systems in this work: Google ASR (commercial) and the ASPIRE model (open-source). We observe 7% absolute improvement in word error rate and 4 point absolute improvement in BLEU score in Google ASR output via our proposed method. We also evaluate ASR error correction via a downstream task of Speaker Diarization that captures speaker style, syntax, structure and semantic improvements we obtain via ASR correction.