# Correcting Automated and Manual Speech Transcription Errors using Warped Language Models

arXiv: https://arxiv.org/abs/2103.14580 · HTML: https://mlsanity.com/paper/2103.14580/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [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)
- [Transformer-based Acoustic Modeling for Hybrid Speech Recognition](https://mlsanity.com/paper/1910.09799/) (2019)
- [Joint Contextual Modeling for ASR Correction and Language Understanding](https://mlsanity.com/paper/2002.00750/) (2020)
- [Feedback-Based Self-Learning in Large-Scale Conversational AI Agents](https://mlsanity.com/paper/1911.02557/) (2019)
- [Pre-Training for Query Rewriting in A Spoken Language Understanding System](https://mlsanity.com/paper/2002.05607/) (2020)
- [Warped Language Models for Noise Robust Language Understanding](https://mlsanity.com/paper/2011.01900/) (2020)
- [L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition](https://mlsanity.com/paper/1910.11496/) (2019)
- [Exploring Methods for the Automatic Detection of Errors in Manual Transcription](https://mlsanity.com/paper/1904.04294/) (2019)

## Built on by (most cited first, 8 of 8)

- ["How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations](https://mlsanity.com/paper/2109.13489/) (2021)
- [A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision](https://mlsanity.com/paper/2306.13114/) (2023)
- [ProGRes: Prompted Generative Rescoring on ASR n-Best](https://mlsanity.com/paper/2409.00217/) (2024)
- [NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning](https://mlsanity.com/paper/2306.12577/) (2023)
- ['Beach' to 'Bitch': Inadvertent Unsafe Transcription of Kids' Content on YouTube](https://mlsanity.com/paper/2203.04837/) (2022)
- [Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting](https://mlsanity.com/paper/2310.13321/) (2023)
- [Robust Unstructured Knowledge Access in Conversational Dialogue with ASR Errors](https://mlsanity.com/paper/2211.03990/) (2022)
- [HTEC: Human Transcription Error Correction](https://mlsanity.com/paper/2309.10089/) (2023)

## Abstract (arXiv)

Masked language models have revolutionized natural language processing systems in the past few years. A recently introduced generalization of masked language models called warped language models are trained to be more robust to the types of errors that appear in automatic or manual transcriptions of spoken language by exposing the language model to the same types of errors during training. In this work we propose a novel approach that takes advantage of the robustness of warped language models to transcription noise for correcting transcriptions of spoken language. We show that our proposed approach is able to achieve up to 10% reduction in word error rates of both automatic and manual transcriptions of spoken language.