Automatic sign language translation process relies mainly on dictionaries of signs to interpret the right meaning of gestures. Due to the lack of large multi sign language dictionaries covering all the aspect of sign languages, the collaborative approach to create signs becomes essential. In fact, the collaborative sign creation process based on Kinect motion capture tool requires the collaboration of non expert users to make sign language dictionaries. However, due to the availability constraint of sign language experts to validate the created signs and the huge amount of signs to be validated manually, the automatic sign language validation process becomes the most suitable solution. In this paper, we present a new automatic and unsupervised sign validation process based on machine learning techniques applied on sign replicas. Given a set of replicas (records) of the same sign created by different non expert sign language user, our main goal is to select the adequate sign records to be used to generate the closest sign signature compared to the one created by sign language expert. For this aim, we present an automatic sign selection and validation solution based on unsupervised clustering of sign motion parameters related to the different sign replicas. We conducted an experimental study to validate 300 ASL signs based on four unsupervised clustering methods, namely, Kernel PCA Kmeans, GMM, Spectral clustering and kernel Kmeans. We concluded that the use our sign validation process using Spectral clustering method allows us to select the right sign replicas to be used to generate the user sign signature. The use of our unsupervised sign validation process onto 3000 ASL sign replicas (300 sign * 10 replicas) lead us to enhance the R2 score average from 0.4830 without sign validation to 0.9123 with sign validation compared to expert sign signature.

自动手语翻译过程主要依靠符号词典来解释手势的正确含义。由于缺乏涵盖手语所有方面的大型多手语词典，创建手语的协作方法变得必不可少。事实上，基于 Kinect 动作捕捉工具的协同手语创建过程需要非专家用户的协作来制作手语词典。然而，由于手语专家验证创建的标志的可用性限制以及需要手动验证的大量标志，自动手语验证过程成为最合适的解决方案。在本文中，我们基于应用于符号副本的机器学习技术提出了一种新的自动和无监督符号验证过程。给定一组由不同的非专家手语用户创建的相同标志的副本（记录），我们的主要目标是选择足够的标志记录用于生成与手语专家创建的标志签名最接近的标志签名。为此，我们提出了一种基于与不同符号副本相关的符号运动参数的无监督聚类的自动符号选择和验证解决方案。我们进行了一项实验研究，基于四种无监督聚类方法验证 300 个 ASL 标志，即 Kernel PCA Kmeans、GMM、Spectral clustering 和 kernel Kmeans。我们得出的结论是，使用我们的签名验证过程使用 Spectral 聚类方法允许我们选择正确的签名副本以用于生成用户签名签名。