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google/red_ace_asr_error_detection_and_correction
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@inproceedings{gekhman-etal-2022-red,
title = "{RED}-{ACE}: Robust Error Detection for {ASR} using Confidence Embeddings",
author = "Gekhman, Zorik and
Zverinski, Dina and
Mallinson, Jonathan and
Beryozkin, Genady",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.180",
doi = "10.18653/v1/2022.emnlp-main.180",
pages = "2800--2808",
abstract = "ASR Error Detection (AED) models aim to post-process the output of Automatic Speech Recognition (ASR) systems, in order to detect transcription errors. Modern approaches usually use text-based input, comprised solely of the ASR transcription hypothesis, disregarding additional signals from the ASR model. Instead, we utilize the ASR system{'}s word-level confidence scores for improving AED performance. Specifically, we add an ASR Confidence Embedding (ACE) layer to the AED model{'}s encoder, allowing us to jointly encode the confidence scores and the transcribed text into a contextualized representation. Our experiments show the benefits of ASR confidence scores for AED, their complementary effect over the textual signal, as well as the effectiveness and robustness of ACE for combining these signals. To foster further research, we publish a novel
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dataset_licence({ "id": "google/red_ace_asr_error_detection_and_correction" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0