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任务型人机对话系统开发平台研究 被引量:3

Research on Task-based Man-machine Dialogue System Development Platform
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摘要 任务型人机对话系统的核心功能在于它可以基于不同用户提供的特定信息数据,为其提供服务,它能够让人们的日常作业变得简单快捷,在实际中有极大的应有价值。任务型人机对话系统能够与人类的自然交互习惯相匹配,在信息交互处理方面具有极强的适应性。笔者从人机对话的简述入手,从模块、层次,两个方面对任务型人机对话系统开发平台及其技术分层进行了相应的分析与论述,以期为任务型人机对话系统平台的未来研发提供有益参考。 The core function of task-based man-machine dialogue system is that it can provide services for different users based on specific information data.It can make people's daily work simple and fast,and has great value in practice.Task-based manmachine dialogue system can match with human's natural interaction habits and has strong adaptability in information interaction processing.Starting from the brief introduction of man-machine dialogue,this paper analyzes and discusses the task-based manmachine dialogue system development platform and its technical layers from the aspects of module,level and two aspects,so as to provide useful reference for the future research and development of the task-based man-machine dialogue system platform.
作者 黄寅 Huang Yin(Chuzhou Branch of Anhui Radio and TV University,Chuzhou Anhui 239000,China)
出处 《信息与电脑》 2020年第6期115-117,共3页 Information & Computer
基金 虚拟角色智能对话生成系统关键技术研究(项目编号:18511105502)。
关键词 任务型人机对话系统 开发平台 信息交互处理 task-based human-computer dialogue system development platform information interaction processing
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  • 1余伶俐,蔡自兴,陈明义.语音信号的情感特征分析与识别研究综述[J].电路与系统学报,2007,12(4):76-84. 被引量:27
  • 2董士海,王横.人机交互.北京:北京大学出版社,2003. 被引量:1
  • 3Dahland G E, Yu Dong, Deng u, Acero A. Context?dependent pre- trained deep neural networks for large?vocabulary speech recognition. IEEE Transactions on Audio, Speech & Language Processing, 2012, 200): 30-42. 被引量:1
  • 4Federico M, Bertoldi N, Cettolo M. Irstlm , An open source toolkit for handling large scale language models/ /Proceedings of the Annual Conference of the International Speech Communication Association (Interopeech), Brisbane, Australia, 2008: 1618-1621. 被引量:1
  • 5Mohri M, Pereira F, Riley M. Weighted finite-state trans?ducers in speech recognition. Computer Speech &. Language, 2002, 16(1): 69-88. 被引量:1
  • 6Senior A, Lei Xin. Fine context, low-rank, softplus deep neural networks for mobile speech recognition/ /Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal ProcessingCICASSP). Florence, Italy, 2014. 被引量:1
  • 7Zen Hei-Ga, Tokuda K, Black A W. Statistical parametric speech synthesis. Speech Communication, 2009, 51(11): 1039-1064. 被引量:1
  • 8WU Y J, Wang R H. Minimum generation error training for hmm-based speech synthesis/ /Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (lCASSP). Toulouse, France, 2006. 被引量:1
  • 9Yu K, Young S. Continuous FO modelling for HMM based statistical speech synthesis. IEEE Transactions on Audio, Speech and Language Processing, 2011,19(5): 1071-1079. 被引量:1
  • 10Zen H, Senior A, Schuster M. Statistical parametric speech synthesis using deep neural networks/ /Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal ProcessingCICASSP). Vancouver, Canada, 2013. 被引量:1

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