# Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries

arXiv: https://arxiv.org/abs/2105.09930 · HTML: https://mlsanity.com/paper/2105.09930/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context](https://mlsanity.com/paper/2005.03191/) (2020)
- [A spelling correction model for end-to-end speech recognition](https://mlsanity.com/paper/1902.07178/) (2019)
- [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)
- [Transformers with convolutional context for ASR](https://mlsanity.com/paper/1904.11660/) (2019)
- [Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments](https://mlsanity.com/paper/1705.10874/) (2017)
- [Feedback-Based Self-Learning in Large-Scale Conversational AI Agents](https://mlsanity.com/paper/1911.02557/) (2019)
- [Percent Change Estimation in Large Scale Online Experiments](https://mlsanity.com/paper/1711.00562/) (2017)

## Built on by (most cited first, 4 of 4)

- [Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI](https://mlsanity.com/paper/2205.00029/) (2022)
- [Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices](https://mlsanity.com/paper/2409.16469/) (2024)
- [FlanEC: Exploring Flan-T5 for Post-ASR Error Correction](https://mlsanity.com/paper/2501.12979/) (2025)
- [ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Features](https://mlsanity.com/paper/2511.21088/) (2025)

## Abstract (arXiv)

As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, Mondegreen, to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts.