教学法在传统的语言教育中是重要的组成部分,而日本的JSL(Japanese as Second Language)教育在发展过程中也受到了教学法发展的影响。通过对日本JSL的教学法的梳理发现,随着教学法日益重视学习者本身,JSL教育研究也开始重视学习者在JSL...教学法在传统的语言教育中是重要的组成部分,而日本的JSL(Japanese as Second Language)教育在发展过程中也受到了教学法发展的影响。通过对日本JSL的教学法的梳理发现,随着教学法日益重视学习者本身,JSL教育研究也开始重视学习者在JSL教育中的地位,并逐步发展出“以学习者为中心”的一体化教育模式。而JSL教育的一体化将成为CSL(Chinese as Second Language)教育未来发展的重要参考。展开更多
Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japane...Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.展开更多
文摘教学法在传统的语言教育中是重要的组成部分,而日本的JSL(Japanese as Second Language)教育在发展过程中也受到了教学法发展的影响。通过对日本JSL的教学法的梳理发现,随着教学法日益重视学习者本身,JSL教育研究也开始重视学习者在JSL教育中的地位,并逐步发展出“以学习者为中心”的一体化教育模式。而JSL教育的一体化将成为CSL(Chinese as Second Language)教育未来发展的重要参考。
基金supported by the Competitive Research Fund of the University of Aizu,Japan.
文摘Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
