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未能得到有效传达，阻碍了其在分析工作流程中的无缝集成。本文介绍ConFides，一个与情报分析师协作开发的可视化分析系统，旨在解决此问题。ConFides通过可视化呈现转录文本的置信度，以辅助探索及AI转录后编辑工作。我们展示了该工具如何协助在分析与探索任务中使用ASR输出的情报分析师，并说明其如何帮助减少对关键信息的误判。同时，我们探讨了在文本数据清洗及人机协作模型透明度提升方面的改进空间。