# Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling

arXiv: https://arxiv.org/abs/1802.02607 · HTML: https://mlsanity.com/paper/1802.02607/

## Key facts

- Cited by 10 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition](https://mlsanity.com/paper/2105.03842/) (2021)
- [FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition](https://mlsanity.com/paper/2109.14420/) (2021)
- [Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models](https://mlsanity.com/paper/2401.01572/) (2024)
- [Hallucination of speech recognition errors with sequence to sequence learning](https://mlsanity.com/paper/2103.12258/) (2021)
- [SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition](https://mlsanity.com/paper/2212.01039/) (2022)
- [Fewer Hallucinations, More Verification: A Three-Stage LLM-Based Framework for ASR Error Correction](https://mlsanity.com/paper/2505.24347/) (2025)
- [Aligning ASR Evaluation with Human and LLM Judgments: Intelligibility Metrics Using Phonetic, Semantic, and NLI Approaches](https://mlsanity.com/paper/2506.16528/) (2025)
- [Revisiting ASR Error Correction with Specialized Models](https://mlsanity.com/paper/2405.15216/) (2024)
- [Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI](https://mlsanity.com/paper/2501.06129/) (2025)
- [Hybrid phonetic-neural model for correction in speech recognition systems](https://mlsanity.com/paper/2102.06744/) (2021)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems often make unrecoverable errors due to subsystem pruning (acoustic, language and pronunciation models); for example pruning words due to acoustics using short-term context, prior to rescoring with long-term context based on linguistics. In this work we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those. The proposed system can exploit long-term context using a neural network language model and can better choose between existing ASR output possibilities as well as re-introduce previously pruned or unseen (out-of-vocabulary) phrases. It provides corrections under poorly performing ASR conditions without degrading any accurate transcriptions; such corrections are greater on top of out-of-domain and mismatched data ASR. Our system consistently provides improvements over the baseline ASR, even when baseline is further optimized through recurrent neural network language model rescoring. This demonstrates that any ASR improvements can be exploited independently and that our proposed system can potentially still provide benefits on highly optimized ASR. Finally, we present an extensive analysis of the type of errors corrected by our system.