@inproceedings{udagawa-etal-2024-robust,
title = "Robust {ASR} Error Correction with Conservative Data Filtering",
author = "Udagawa, Takuma and
Suzuki, Masayuki and
Muraoka, Masayasu and
Kurata, Gakuto",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.20",
pages = "256--266",
abstract = "Error correction (EC) based on large language models is an emerging technology to enhance the performance of automatic speech recognition (ASR) systems.Generally, training data for EC are collected by automatically pairing a large set of ASR hypotheses (as sources) and their gold references (as targets).However, the quality of such pairs is not guaranteed, and we observed various types of noise which can make the EC models brittle, e.g. inducing overcorrection in out-of-domain (OOD) settings.In this work, we propose two fundamental criteria that EC training data should satisfy: namely, EC targets should (1) improve linguistic acceptability over sources and (2) be inferable from the available context (e.g. source phonemes).Through these criteria, we identify low-quality EC pairs and train the models not to make any correction in such cases, the process we refer to as conservative data filtering.In our experiments, we focus on Japanese ASR using a strong Conformer-CTC as the baseline and finetune Japanese LLMs for EC.Through our evaluation on a suite of 21 internal benchmarks, we demonstrate that our approach can significantly reduce overcorrection and improve both the accuracy and quality of ASR results in the challenging OOD settings.",
}
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<abstract>Error correction (EC) based on large language models is an emerging technology to enhance the performance of automatic speech recognition (ASR) systems.Generally, training data for EC are collected by automatically pairing a large set of ASR hypotheses (as sources) and their gold references (as targets).However, the quality of such pairs is not guaranteed, and we observed various types of noise which can make the EC models brittle, e.g. inducing overcorrection in out-of-domain (OOD) settings.In this work, we propose two fundamental criteria that EC training data should satisfy: namely, EC targets should (1) improve linguistic acceptability over sources and (2) be inferable from the available context (e.g. source phonemes).Through these criteria, we identify low-quality EC pairs and train the models not to make any correction in such cases, the process we refer to as conservative data filtering.In our experiments, we focus on Japanese ASR using a strong Conformer-CTC as the baseline and finetune Japanese LLMs for EC.Through our evaluation on a suite of 21 internal benchmarks, we demonstrate that our approach can significantly reduce overcorrection and improve both the accuracy and quality of ASR results in the challenging OOD settings.</abstract>
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%0 Conference Proceedings
%T Robust ASR Error Correction with Conservative Data Filtering
%A Udagawa, Takuma
%A Suzuki, Masayuki
%A Muraoka, Masayasu
%A Kurata, Gakuto
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F udagawa-etal-2024-robust
%X Error correction (EC) based on large language models is an emerging technology to enhance the performance of automatic speech recognition (ASR) systems.Generally, training data for EC are collected by automatically pairing a large set of ASR hypotheses (as sources) and their gold references (as targets).However, the quality of such pairs is not guaranteed, and we observed various types of noise which can make the EC models brittle, e.g. inducing overcorrection in out-of-domain (OOD) settings.In this work, we propose two fundamental criteria that EC training data should satisfy: namely, EC targets should (1) improve linguistic acceptability over sources and (2) be inferable from the available context (e.g. source phonemes).Through these criteria, we identify low-quality EC pairs and train the models not to make any correction in such cases, the process we refer to as conservative data filtering.In our experiments, we focus on Japanese ASR using a strong Conformer-CTC as the baseline and finetune Japanese LLMs for EC.Through our evaluation on a suite of 21 internal benchmarks, we demonstrate that our approach can significantly reduce overcorrection and improve both the accuracy and quality of ASR results in the challenging OOD settings.
%U https://aclanthology.org/2024.emnlp-industry.20
%P 256-266
Markdown (Informal)
[Robust ASR Error Correction with Conservative Data Filtering](https://aclanthology.org/2024.emnlp-industry.20) (Udagawa et al., EMNLP 2024)
ACL
- Takuma Udagawa, Masayuki Suzuki, Masayasu Muraoka, and Gakuto Kurata. 2024. Robust ASR Error Correction with Conservative Data Filtering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 256–266, Miami, Florida, US. Association for Computational Linguistics.