Sign language detection, identifying if someone is signing or not, is becoming crucially important for its applications in remote conferencing software and for selecting useful sign data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detection estimate overly positive results that do not generalize well due to signer overlap between train and test partitions. We quantify this with a detailed analysis of the effect of signer overlap on current sign detection benchmark data sets. Comparing accuracy with and without overlap on the DGS corpus and Signing in the Wild, we observed a relative decrease in accuracy of 4.17% and 6.27%, respectively. Furthermore, we propose new data set partitions that are free of overlap and allow for more realistic performance assessment. We hope this work will contribute to improving the accuracy and generalization of sign language detection systems.

翻译：手语检测——即识别某人是否正在使用手语——在远程会议软件以及为训练手语识别或翻译任务筛选有用手语数据中变得至关重要。我们认为，当前手语检测基准数据集因训练集和测试集之间存在手语者重叠，从而估计出过于乐观且难以泛化的结果。我们通过对当前手语检测基准数据集中手语者重叠的影响进行详细分析来量化这一现象。在DGS语料库和野外手语数据集上比较有无重叠情况下的准确率时，我们观察到准确率分别相对下降了4.17%和6.27%。此外，我们提出了无重叠的新数据集划分方案，从而能够进行更真实的性能评估。我们希望这项工作能有助于提高手语检测系统的准确性和泛化能力。