临床BERTScore：临床环境中自动语音识别性能的改进度量
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings
AI总结：
针对临床ASR转录错误，提出CBERTScore度量，更重罚医学相关错误，并构建首个公开的临床医生转录偏好基准CTP，验证其与临床偏好高度一致。
摘要：
在医疗背景下，自动语音识别（ASR）有潜力节省时间、降低成本、提高报告准确性并减少医生职业倦怠。然而，由于避免医学相关转录错误的重要性，医疗行业对该技术的采用速度较慢。在本研究中，我们提出了临床BERTScore（CBERTScore），这是一种ASR度量，它对临床相关错误的惩罚程度高于其他错误。我们证明，与其他度量（如WER、BLEU、METEOR等）相比，该度量在医学句子上更贴近临床医生的偏好，有时差距很大。我们收集了一个包含18位临床医生对149个真实医学句子偏好的基准数据集，称为临床医生转录偏好基准（CTP），并将其公开提供给社区，以进一步开发具有临床意识的ASR度量。据我们所知，这是首个此类公开数据集。我们证明CBERTScore更贴近临床医生的偏好。
英文摘要：
Automatic Speech Recognition (ASR) in medical contexts has the potential to save time, cut costs, increase report accuracy, and reduce physician burnout. However, the healthcare industry has been slower to adopt this technology, in part due to the importance of avoiding medically-relevant transcription mistakes. In this work, we present the Clinical BERTScore (CBERTScore), an ASR metric that penalizes clinically-relevant mistakes more than others. We demonstrate that this metric more closely aligns with clinician preferences on medical sentences as compared to other metrics (WER, BLUE, METEOR, etc), sometimes by wide margins. We collect a benchmark of 18 clinician preferences on 149 realistic medical sentences called the Clinician Transcript Preference benchmark (CTP) and make it publicly available for the community to further develop clinically-aware ASR metrics. To our knowledge, this is the first public dataset of its kind. We demonstrate that CBERTScore more closely matches what clinicians prefer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
