Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Training Personal Robots Using Natural Language Instruction
IEEE Intelligent Systems
Compiling language models from a linguistically motivated unification grammar
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Practical issues in compiling typed unification grammars for speech recognition
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Do CFG-based language models need agreement constraints?
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An inference-based approach to dialogue system design
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
From ubgs to cfgs a practical corpus-driven approach
Natural Language Engineering
Training Statistical Language Models from Grammar-Generated Data: A Comparative Case-Study
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Making speech look like text in the Regulus development environment
GEAF '08 Proceedings of the Workshop on Grammar Engineering Across Frameworks
Speech recognition grammar compilation in Grammatical Framework
SLP '07 Proceedings of the Workshop on Grammar-Based Approaches to Spoken Language Processing
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
| Hi-index | 0.02 |
In this paper a method to compile unification grammars into speech recognition packages is presented, and in particular, rules are specified to transfer the compositional semantics stated in unification grammars into speech recognition grammars. The resulting compiler creates a context-free backbone of the unification grammar, eliminates left-recursive productions and removes redundant grammar rules. The method was tested on a medium-sized unification grammar for English using Nuance speech recognition software on a corpus of 131 utterances of 12 different speakers. Results showed no significant computational overhead with respect to speech recognition performances for speech recognition grammar with compositional semantics compared to grammars without.