Speech and gestures for graphic image manipulation
CHI '89 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Two-handed gesture in multi-modal natural dialog
UIST '92 Proceedings of the 5th annual ACM symposium on User interface software and technology
WordNet: a lexical database for English
Communications of the ACM
A maximum entropy approach to natural language processing
Computational Linguistics
An interactive environment for virtual manufacturing: the virtual work bench
Computers in Industry - Special issue on multimedia in manufacturing
Foundations of statistical natural language processing
Foundations of statistical natural language processing
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Speech recognition: theory and C++ implementation
Speech recognition: theory and C++ implementation
Speech Synthesis and Recognition
Speech Synthesis and Recognition
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
An Approach to 3D Digital Design Free Hand Form Generation
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Computer Speech: Recognition, Compression, Synthesis (Springer Series in Information Sciences)
Computer Speech: Recognition, Compression, Synthesis (Springer Series in Information Sciences)
Examining the effectiveness of real-time query expansion
Information Processing and Management: an International Journal
Combining fields for query expansion and adaptive query expansion
Information Processing and Management: an International Journal
Word Sense Disambiguation Using Extended WordNet
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Manipulation of CAD surface models with haptics based on shape control functions
Computer-Aided Design
Quadtree based mouse trajectory analysis for efficacy evaluation of voice-enabled CAD
VECIMS'09 Proceedings of the 2009 IEEE international conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems
Multi-term web query expansion using wordnet
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Classification of primitive shapes using brain-computer interfaces
Computer-Aided Design
New intelligent interactive automated systems for design of machine elements and assemblies
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Hi-index | 0.00 |
Voice based human-computer interactions have raised much interest and found various applications. Some extant voice based interactions only support voice commands with fixed vocabularies or preset expressions. This paper is motivated to investigate an approach to implement a flexible voice-enabled CAD system, where users are no longer constrained by predefined commands. Designers can, to a much more flexible degree, communicate with CAD modelers using natural language conversations. To accomplish this, a knowledge-guided approach is proposed to infer the semantics of voice input. The semantic inference is formulated as a template matching problem, where the semantic units parsed from voice input are the ''samples'' to be inspected and the semantic units in the predefined library are the feature templates. The proposed behavioral glosses, together with CAD-specific synonyms, hyponyms and hypernyms are extensively used in the parsing of semantic units and the subsequent template matching. Using such sources of knowledge, all the semantically equivalent expressions can be mapped to the same command set, and the Voice-enabled Computer Aided Design (VeCAD) system is then capable of processing new expressions it has never encountered and inferring/understanding the semantics at runtime. Experiments show that this knowledge-guided approach is helpful to enhance the robustness of semantic inference and can effectively eliminate the chance of overestimations and underestimations in design intent interpretation.