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.

本研究提出了 SeMaScore，它使用基于片段的映射和评分算法生成，并作为自动语音识别任务的评估指标。SeMaScore 利用错误率和更稳健的相似性得分，实验证明我们算法的评分生成优于现有的 BERTscore。我们的实验结果显示 SeMaScore 与专家人工评估、信噪比水平和其他自然语言指标相符。我们在计算速度上比 BERTscore 提高了 41 倍。总体而言，我们证明 SeMaScore 在涉及非典型语音模式的真实世界情况下是一种更可靠的评估指标。