  In the realm of automatic speech recognition (ASR), the quest for models that
not only perform with high accuracy but also offer transparency in their
decision-making processes is crucial. The potential of quality estimation (QE)
metrics is introduced and evaluated as a novel tool to enhance explainable
artificial intelligence (XAI) in ASR systems. Through experiments and analyses,
the capabilities of the NoRefER (No Reference Error Rate) metric are explored
in identifying word-level errors to aid post-editors in refining ASR
hypotheses. The investigation also extends to the utility of NoRefER in the
corpus-building process, demonstrating its effectiveness in augmenting datasets
with insightful annotations. The diagnostic aspects of NoRefER are examined,
revealing its ability to provide valuable insights into model behaviors and
decision patterns. This has proven beneficial for prioritizing hypotheses in
post-editing workflows and fine-tuning ASR models. The findings suggest that
NoRefER is not merely a tool for error detection but also a comprehensive
framework for enhancing ASR systems' transparency, efficiency, and
effectiveness. To ensure the reproducibility of the results, all source codes
of this study are made publicly available.
