  Word Error Rate (WER) has been the predominant metric used to evaluate the
performance of automatic speech recognition (ASR) systems. However, WER is
sometimes not a good indicator for downstream Natural Language Understanding
(NLU) tasks, such as intent recognition, slot filling, and semantic parsing in
task-oriented dialog systems. This is because WER takes into consideration only
literal correctness instead of semantic correctness, the latter of which is
typically more important for these downstream tasks. In this study, we propose
a novel Semantic Distance (SemDist) measure as an alternative evaluation metric
for ASR systems to address this issue. We define SemDist as the distance
between a reference and hypothesis pair in a sentence-level embedding space. To
represent the reference and hypothesis as a sentence embedding, we exploit
RoBERTa, a state-of-the-art pre-trained deep contextualized language model
based on the transformer architecture. We demonstrate the effectiveness of our
proposed metric on various downstream tasks, including intent recognition,
semantic parsing, and named entity recognition.
