| 基于音系学模型的手语理解 |
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| 引用本文： | 姚登峰,江铭虎,阿布都克力木·阿布力孜,李晗静,哈里旦木·阿布都克里木.基于音系学模型的手语理解[J].中文信息学报,2018,32(1):59-67. |
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| 作者姓名： | 姚登峰 江铭虎 阿布都克力木·阿布力孜 李晗静 哈里旦木·阿布都克里木 |
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| 作者单位： | 1.北京市信息服务工程重点实验室(北京联合大学),北京 100101;2.清华大学 人文学院 计算语言学实验室、心理学与认知科学研究中心,北京 100084;3.清华大学 计算机科学与技术系 智能技术与系统国家重点实验室,北京 100084 |
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| 基金项目： | 国家自然科学基金 (61433015,91420202,61602040);国家社会科学基金(14ZDB154);教育部人文社会科学研究规划基金(14YJC740104);国家语委重点项目(ZDI135-31);北京市属高校高水平教师队伍建设支持计划高水平创新团队建设计划(IDHT20170511);北京市教委科技计划项目(KM201711417006);清华大学自主科研项目两岸清华大学专项(20161080056) |
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| 摘 要： | 该文试图模拟人脑处理手势信号的过程,设计了一个混合的深层神经网络模型来解决基于音系学模型的手语理解问题,即手语音韵信息到文本转换的问题。为此该文首先综合了手语语言学里同时性和序列性这两个观点的长处,提出了一个手语音系学的改进模型,并针对难点设计了基于音系学模型的手语理解算法。直接从语言学的音韵特征推断手语文本,相比从视觉特征推断出手语文本是一个很大的飞跃。实验验证了该认知计算技术的有效性,为实现类人智能奠定了技术基础。
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| 关 键 词： | 音韵参数 手语 深度学习 音系学模型 |
Sign Language Understanding Based on Phonology Model |
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| YAO Dengfeng,JIANG Minghu,Abudoukelimu Abulizi,LI Hanjing,Halidanmu Abudukelimu.Sign Language Understanding Based on Phonology Model[J].Journal of Chinese Information Processing,2018,32(1):59-67. |
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| Authors: | YAO Dengfeng JIANG Minghu Abudoukelimu Abulizi LI Hanjing Halidanmu Abudukelimu |
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| Affiliation: | 1. Beijing Key Lab of Information Service Engineering (Beijing Union University), Beijing 100101, China; 2. Lab of Computational Linguistics, School of Humanities, Center for Psychology and Cognitive Science, Tsinghua University, Beijing 100084, China; 3. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Sci. and Tech., Tsinghua University, Beijing 100084, China |
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| Abstract: | This paper tries to simulate the process of sign processing in the human brain, and designs a hybrid neural network model to solve the sign language understanding based on phonological model, i.e. converting the phonological information of hand to Chinese text. We first integrate the advantages of the two perspectives of simultaneity and sequence in sign language, and propose an improved model of sign language phonology. The first-perception first-comprehension algorithm is designed based on the cognitive mechanism of the brain, which processes Chinese text directly from phonological features of the sign that can act as linguistic features. Compared with the traditional method that deduces Chinese text from graphic features, this algorithm represents tremendous progress in cognitive computing. Experimental results verify the feasibility of the intelligent cognitive technology, which lays a technical foundation to realize robot intelligence. |
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| Keywords: | phonological parameters sign language deep learning phonology model |
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