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首页> 外文期刊>Computer speech and language >Unsupervised sign language validation process based on hand-motion parameter clustering
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Unsupervised sign language validation process based on hand-motion parameter clustering

机译：无监督的手语验证过程基于手动运动参数聚类

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摘要

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.
机译：自动标志语言翻译过程主要依赖于迹象的词典来解释手势的正确意义。由于缺乏大型多牌语言词典，涵盖了符号语言的所有方面，创建迹象的协同方法变得至关重要。事实上，基于Kinect Motion Capture工具的协作标志创建过程需要非专家用户的协作来进行手语词典。但是，由于手语专家的可用性约束来验证创建的标志和手动验证的大量标志，自动标志语言验证过程成为最合适的解决方案。在本文中，我们提出了一种基于机器学习技术的新的自动和无监督标志验证过程，应用于标志副本。给定一组副本（记录）由不同非专家手语媒体创建的相同标志，我们的主要目标是选择用于生成最接近的符号签名的足够的符号记录，与由Sign Language Expert创建的那个相比。为此目的，我们基于与不同的符号副本相关的符号动作参数的无监督群集提供自动标志选择和验证解决方案。我们进行了一个实验研究，以基于四个无监督的聚类方法，即内核PCA kmeans，GMM，Spectral Clustering和Kernel Kmeans的验证了300个ASL标志。我们得出结论，使用Spectral Clustering方法使用我们的符号验证流程允许我们选择要用于生成用户签名签名的右侧副本副本。使用我们无监督的标志验证过程到3000 ASL标志副本（300签名* 10副本）引导我们从0.4830增强R2得分平均值，而无需符号验证，与专家标志签名相比，标志验证。

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