Automatic translation of formal data specifications to voice data-input applications.

This thesis introduces a complete solution for automatic translation of formal data specifications to voice data-input applications. The objective of the research is to automatically generate applications for inputting data through speech from specifications of the structure of the data. The formal data specifications are XML DTDs. A new formalization called Grammar-DTD (G-DTD) is introduced as an extended DTD that contains grammars to describe valid values of the DTD elements and attributes. G-DTDs facilitate the automatic generation of Voice XML applications that correspond to the original DTD structure. The development of the automatic application-generator included identifying constraints on the G-DTD to ensure a feasible translation, using predicate calculus to build a knowledge base of inference rules that describes the mapping procedure, and writing an algorithm for the automatic translation based on the inference rules.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .H355. Source: Masters Abstracts International, Volume: 45-01, page: 0354. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Research Issues for the Next Generation Spoken Dialogue Systems

. In this paper we present extensions to the spoken dialogue system EVAR which are crucial issues for the next generation dialogue systems. EVAR was developed at the University of Erlangen. In 1994, it became accessible over telephone line and could answer inquiries in the German language about German InterCity train connections. It has since been continuously improved and extended, including some unique features, such as the processing of out--of--vocabulary words and a flexible dialogue strategy that adapts to the quality of the recognition of the user input. 1 Introduction The spoken dialogue system EVAR was developed at the University of Erlangen over a period of almost 20 years. Different system architectures have been implemented and evaluated, and intensive research has been performed in the areas of word recognition, linguistic analysis, knowledge representation, dialogue management, and prosodic analysis. To our knowledge, EVAR was the first spoken dialogue system in..

Research Issues for the Next Generation Spoken Dialogue Systems Revisited

In this paper we take a second look at current research issues for conversational dialogue systems addressed in [17]. We look at two systems, a movie information and a stock information system which were built based on the experiences with the train information system Evar, described in [17]