mlsanityData to 7 Oct 2026

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

arXiv 2105.09930 ↗First posted 20 May 2021cs.SD cs.CL cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 20 May 2021.

Builds on

10 of 25 references.

References · 10

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Contextcited by 96
  4. 2019A spelling correction model for end-to-end speech recognitioncited by 63
  5. 2019Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Modelcited by 52
  6. 2020ASR Error Correction and Domain Adaptation Using Machine Translationcited by 48
  7. 2019Transformers with convolutional context for ASRcited by 44
  8. 2017Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developmentscited by 30
  9. 2019Feedback-Based Self-Learning in Large-Scale Conversational AI Agentscited by 15
  10. 2017Percent Change Estimation in Large Scale Online Experimentscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2.

4 papers.

  1. 2022Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AIcited by 3
  2. 2024Spelling Correction through Rewriting of Non-Autoregressive ASR Latticescited by 1
  3. 2025FlanEC: Exploring Flan-T5 for Post-ASR Error Correctioncited by 1
  4. 2025ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Featurescited by 0