Confidence scores of automatic speech recognition (ASR) outputs are often inadequately communicated, preventing its seamless integration into analytical workflows. In this paper, we introduce ConFides, a visual analytic system developed in collaboration with intelligence analysts to address this issue. ConFides aims to aid exploration and post-AI-transcription editing by visually representing the confidence associated with the transcription. We demonstrate how our tool can assist intelligence analysts who use ASR outputs in their analytical and exploratory tasks and how it can help mitigate misinterpretation of crucial information. We also discuss opportunities for improving textual data cleaning and model transparency for human-machine collaboration.

翻译：自动语音识别（ASR）输出的置信度分数往往无法有效传达，阻碍了其与分析工作流的无缝集成。本文介绍ConFides——一个与情报分析师合作开发的可视分析系统，旨在解决该问题。ConFides通过可视化转录文本的置信度，辅助用户进行探索式分析及AI转录后编辑。我们展示了该工具如何协助采用ASR输出进行分析与探索任务的情报分析师，并减少对关键信息的错误解读。同时，本文探讨了提升文本数据清洗效果与模型透明度以促进人机协作的潜在方向。