A scenic-spot introduction-task-oriented 3D virtual human spoken dialogue system-- EasyGuide is introduced. The system includes five modules： natural language processing, task do- main knowledge database, dialogue ma...A scenic-spot introduction-task-oriented 3D virtual human spoken dialogue system-- EasyGuide is introduced. The system includes five modules： natural language processing, task do- main knowledge database, dialogue management, voice processing and 3D virtual human text-to-vis- ual speech synthesis. In the first module, dictionary construction along with sentence analysis and semantic representation axe illustrated specifically. A tree-structured knowledge database is designed for the task domain. A novel framework based on the keyword analysis and context constraints is proposed as the dialogue management. As for voice processing module, a software development kit which performs speech recognition and synthesis is introduced briefly. In the last module, 3D viseme synthesis is explained with examples and a text-driven facial animation system is presented. Evalua- tion results show that the system can achieve satisfactory performance.展开更多
Statistical dialogue management is the core of cognitive spoken dialogue systems （SDS） and has attracted great research interest. In recent years, SDS with the ability of evolution is of particular interest and beco...Statistical dialogue management is the core of cognitive spoken dialogue systems （SDS） and has attracted great research interest. In recent years, SDS with the ability of evolution is of particular interest and becomes the cuttingedge of SDS research. Dialogue state tracking （DST） is a process to estimate the distribution of the dialogue states at each dialogue turn, given the previous interaction history. It plays an important role in statistical dialogue management. To provide a common testbed for advancing the research of DST, international DST challenges （DSTC） have been organised and well-attended by major SDS groups in the world. This paper reviews recent progresses on rule-based and statistical approaches during the challenges. In particular, this paper is focused on evolvable DST approaches for dialogue domain extension. The two primary aspects for evolution, semantic parsing and tracker, are discussed. Semantic enhancement and a DST framework which bridges rule-based and statistical models are introduced in detail. By effectively incorporating prior knowledge of dialogue state transition and the ability of being data-driven, the new framework supports reliable domain extension with little data and can continuously improve with more data available. This makes it excellent candidate for DST evolution. Experiments show that the evolvable DST approaches can achieve the state-of-the-art performance and outperform all previously submitted trackers in the third DSTC.展开更多
基金Supported by the Ministerial Level Advanced Research Foundation(404050301.4)the National Natural Science Foundation of hina(60605015)
文摘A scenic-spot introduction-task-oriented 3D virtual human spoken dialogue system-- EasyGuide is introduced. The system includes five modules： natural language processing, task do- main knowledge database, dialogue management, voice processing and 3D virtual human text-to-vis- ual speech synthesis. In the first module, dictionary construction along with sentence analysis and semantic representation axe illustrated specifically. A tree-structured knowledge database is designed for the task domain. A novel framework based on the keyword analysis and context constraints is proposed as the dialogue management. As for voice processing module, a software development kit which performs speech recognition and synthesis is introduced briefly. In the last module, 3D viseme synthesis is explained with examples and a text-driven facial animation system is presented. Evalua- tion results show that the system can achieve satisfactory performance.
文摘Statistical dialogue management is the core of cognitive spoken dialogue systems （SDS） and has attracted great research interest. In recent years, SDS with the ability of evolution is of particular interest and becomes the cuttingedge of SDS research. Dialogue state tracking （DST） is a process to estimate the distribution of the dialogue states at each dialogue turn, given the previous interaction history. It plays an important role in statistical dialogue management. To provide a common testbed for advancing the research of DST, international DST challenges （DSTC） have been organised and well-attended by major SDS groups in the world. This paper reviews recent progresses on rule-based and statistical approaches during the challenges. In particular, this paper is focused on evolvable DST approaches for dialogue domain extension. The two primary aspects for evolution, semantic parsing and tracker, are discussed. Semantic enhancement and a DST framework which bridges rule-based and statistical models are introduced in detail. By effectively incorporating prior knowledge of dialogue state transition and the ability of being data-driven, the new framework supports reliable domain extension with little data and can continuously improve with more data available. This makes it excellent candidate for DST evolution. Experiments show that the evolvable DST approaches can achieve the state-of-the-art performance and outperform all previously submitted trackers in the third DSTC.
