@inproceedings{c25eb2b8ef0b4f759b86924825d4ec68,
title = "Correcting automated and manual speech transcription errors using warped language models",
abstract = "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 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 the training of language models. 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.",
author = "Mahdi Namazifar and John Malik and Li, {Li Erran} and Gokhan Tur and T{\"u}r, {Dilek Hakkani}",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 ISCA.; 22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021 ; Conference date: 30-08-2021 Through 03-09-2021",
year = "2021",
doi = "10.21437/Interspeech.2021-591",
language = "English (US)",
series = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
publisher = "International Speech Communication Association",
pages = "921--925",
booktitle = "22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021",
}