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基于云平台的智能语音交互机器人设计 被引量:17

Design of Intelligent Voice Interactive Robot based on Cloud Platform
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摘要 现有的语音交互机器人多采用用户提问、机器人回答的单向交流方式,人机交互的智能性和灵活性较差。本文研究运用树莓派(Raspberry Pi)计算机和配套的语音板作为硬件载体,融合语音唤醒、语音识别、语音合成、自然语言处理等人工智能技术,调用科大讯飞开放云平台、在线图灵机器人,搭建一种基于云平台的智能语音交互机器人系统,并结合自主开发的本地知识库和问题库,使智能语音交互机器人能够根据不同环境与任务需求实现双向互动交流,实现由机器人采集信息和交流反馈,以提供高适应性的无接触人机语音交互服务。 Existing voice interactive robots mostly use user questions and the one-way communication method of robot answers,which is less intelligent and flexible in human-computer interaction.This paper proposes to build an intelligent voice interactive robot system based on cloud platform.The proposed system uses Raspberry Pi computer and the supporting voice board as hardware carriers,and integrates artificial intelligence technologies such as voice wake-up,voice recognition,speech synthesis,natural language processing.It also makes use of the services of IFLYTEK open cloud platform and online Turing robot.Combined with self-developed local knowledge base and question library,the intelligent voice interactive robot can conduct two-way interactive communication according to different environment and task requirements,collect information,and exchange feedback.It provides highly adaptable contactless human-machine voice interaction service.
作者 何松 黄维 吴昔遥 周曾豪 杨东泽 HE Song;HUANG Wei;WU Xiyao;ZHOU Zenghao;YANG Dongze(Air Force Early Warning Academy,Wuhan 430019,China)
机构地区 空军预警学院
出处 《软件工程》 2021年第4期55-59,共5页 Software Engineering
关键词 人工智能 自然语言处理 语音交互机器人 树莓派 云平台 artificial intelligence natural language processing voice interactive robot Raspberry Pi cloud platform
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  • 1梅栴,刘纪红,张振川.一种基于SPCE061A的机器人语音交互系统的设计与实现[J].微计算机应用,2005,26(4):485-487. 被引量:5
  • 2吴敏.中文语音标准化现状与发展趋势[J].信息技术与标准化,2007(1):7-10. 被引量:3
  • 3Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural computa- tion, 2006,18(7) :1527-1554.
  • 4Arel I, Rose D C, Karnowski T P. Deep machine learning-A new frontier in artificial intelligence re- search[J]. Computational Intelligence Magazine, IEEE, 2010,5(4) :13-18.
  • 5Deng L. An overview of deep-structured learning for information processing[C]//Proc Asian- Pacific Sig nal and Information Processing-Annual Summit and Conference (APSIPA-ASC). Xi'an, China: [s. n. ], 2011.
  • 6Bengio Y. Learning deep architectures for AI[J]. Foundations and Trends in Machine Learning, 2009, 2(1) :1-127.
  • 7Hinton G E. Training products of experts by minimi- zing contrastive divergenee[J]. Neural Computation, 2002,14(8): 1771-1800.
  • 8Baker J, Deng L, Glass J, et al. Developments and directions in speech recognition and understanding, Part 1[J]. Signal Processing Magazine, IEEE, 2009, 26(3) :75-80.
  • 9Yu D, Deng L. Deep learning and its applications to signal and information processing[J]. Signal Process ing Magazine, IEEE, 2011,28(1) : 145-154. H.
  • 10opfield J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the National Academy of Sciences, 1982,79(8):2554-2558.

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