Experimental results on large-vocabulary continuous speech recognition and understanding
- 6 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 414-417 vol.1
- https://doi.org/10.1109/icassp.1988.196606
Abstract
A continuous speech recognition and understanding system is presented that accepts queries about a restricted geographical domain, expressed in free but syntactically correct natural language, with a lexicon of the order of one thousand words. A lattice of word candidates hypothesized by the speaker dependent recognition level is the interface to an understanding module that performs the syntactic and semantic analysis. The recognition subsystem generates word hypotheses by exploiting hidden Markov models of sub-word units. Bottom-up constraints are also introduced to restrict the set of candidate words. The understanding module determines the most likely sequence of words and represents its meaning in a parse-tree suitable to access a database. It makes use of a modified caseframe analysis driven by the word hypotheses likelihood scores. The results of a set of experiments performed in 150 sentences collected from one speaker are given.Keywords
This publication has 4 references indexed in Scilit:
- Interaction between fast lexical access and word verification in large vocabulary continuous speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- The role of semantic processing in an automatic speech understanding systemPublished by Association for Computational Linguistics (ACL) ,1986
- Optimal search strategies for speech understanding controlArtificial Intelligence, 1982
- Noah-A Bottom-Up Word Hypothesizer for Large-Vocabulary Speech Understanding SystemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1981