  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.
