Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Augmented role filling capabilities for semantic interpretation of spoken language
HLT '91 Proceedings of the workshop on Speech and Natural Language
Experience with a stack decoder-based HMM CSR and back-OFF N-gram language models
HLT '91 Proceedings of the workshop on Speech and Natural Language
HLT '90 Proceedings of the workshop on Speech and Natural Language
Algorithms for an optimal A search and linearizing the search in the stack decoder
HLT '90 Proceedings of the workshop on Speech and Natural Language
Opportunities for advanced speech processing in military computer-based systems
HLT '90 Proceedings of the workshop on Speech and Natural Language
The Lincoln large-vocabulary HMM CSR
HLT '91 Proceedings of the workshop on Speech and Natural Language
HLT '91 Proceedings of the workshop on Speech and Natural Language
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Word graphs: an efficient interface between continuous-speech recognition and language understanding
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
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Spoken Language Systems will require integration of continuous speech recognition and natural language processing. This is a proposed specification for an interface between a continuous speech recognizer (CSR) and a natural language processor (NLP) to form a spoken language system. Both components are integrated with a stack controller and contribute to the search control. The specification also defines a "Top-N" mode in which a "first part" outputs a list of top N scored sentences for postprocessing by a "second part". An additional use for this specification might be NLP evaluation testing: a common simulated CSR could be interfaced to each site's NLP to provide identical testing environments.