High level knowledge sources in usable speech recognition systems
Communications of the ACM
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Efficient Parsing for Natural Language: A Fast Algorithm for Practical Systems
Generalized probabilistic LR parsing of natural language (Corpora) with unification-based grammars
Computational Linguistics - Special issue on using large corpora: I
Intonational boundaries, speech repairs and discourse markers: modeling spoken dialog
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Parsing spoken language: a semantic caseframe approach
COLING '86 Proceedings of the 11th coference on Computational linguistics
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Human understanding of spoken language appears to integrate the use of contextual expectations with acoustic level perception in a tightly-coupled, sequential fashion. Yet computer speech understanding systems typically pass the transcript produced by a speech recognizer into a natural language parser with no integration of acoustic and grammatical constraints. One reason for this is the complexity of implementing that integration. To address this issue we have created a robust, semantic parser as a single finite-state machine (FSM). As such, its run-time action is less complex than other robust parsers that are based on either chart or generalized left-right (GLR) architectures. Therefore, we believe it is ultimately more amenable to direct integration with a speech decoder.