  In this study, we present SeMaScore, generated using a segment-wise mapping
and scoring algorithm that serves as an evaluation metric for automatic speech
recognition tasks. SeMaScore leverages both the error rate and a more robust
similarity score. We show that our algorithm's score generation improves upon
the state-of-the-art BERTscore. Our experimental results show that SeMaScore
corresponds well with expert human assessments, signal-to-noise ratio levels,
and other natural language metrics. We outperform BERTscore by 41x in metric
computation speed. Overall, we demonstrate that SeMaScore serves as a more
dependable evaluation metric, particularly in real-world situations involving
atypical speech patterns.
