# Open Challenge for Correcting Errors of Speech Recognition Systems

arXiv: https://arxiv.org/abs/2001.03041 · HTML: https://mlsanity.com/paper/2001.03041/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction](https://mlsanity.com/paper/2210.10442/) (2022)
- [Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2106.01609/) (2021)
- [Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction](https://mlsanity.com/paper/2306.17447/) (2023)
- [Type-Driven Multi-Turn Corrections for Grammatical Error Correction](https://mlsanity.com/paper/2203.09136/) (2022)
- [Tag and correct: high precision post-editing approach to correction of speech recognition errors](https://mlsanity.com/paper/2406.07589/) (2024)

## 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.