Open Challenge for Correcting Errors of Speech Recognition Systems
Not on the map. Why?
Abstract (arXiv)
The paper announces the new long-term challenge for improving the performance of automatic speech recognition systems. The goal of the challenge is to investigate methods of correcting the recognition results on the basis of previously made errors by the speech processing system. The dataset prepared for the task is described and evaluation criteria are presented.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1.
5 papers.
- 2022Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correctioncited by 25
- 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correctioncited by 19
- 2023Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correctioncited by 13
- 2022Type-Driven Multi-Turn Corrections for Grammatical Error Correctioncited by 9
- 2024Tag and correct: high precision post-editing approach to correction of speech recognition errorscited by 0
- 2024Tag and correct: high precision post-editing approach to correction of speech recognition errors11 Jun 2024
- 2023Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction30 Jun 2023
- 2022Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction19 Oct 2022
- 2022Type-Driven Multi-Turn Corrections for Grammatical Error Correction17 Mar 2022
- 2021Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction3 Jun 2021