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 propose to 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 AED dataset consisting of ASR outputs on
the LibriSpeech corpus with annotated transcription errors.

该研究提出了一种基于自动语音识别 (ASR) 系统的置信得分，改善 ASR 误差检测 (AED) 模型的性能，并证明了这种置信得分的补充作用以及 ASR Confidence Embedding 的有效性和鲁棒性，并发布了一个包含有标记的 LibriSpeech 语料库的 ASR 输出的新型 AED 数据集。