Referential semantic language modeling for data-poor domains

Stephen Wu, Lane Schwartz, William Schuler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes a referential semantic language model that achieves accurate recognition in user-defined domains with no available domain-specific training corpora. This model is interesting in that, unlike similar recent systems, it exploits context dynamically, using incremental processing and limited stack memory of an HMM-like time series model to constrain search.

Original languageEnglish (US)
Title of host publicationInternational Conference on Acoustics, Speech, and Signal Processing
Pages5085-5088
Number of pages4
DOIs
StatePublished - Sep 16 2008
Externally publishedYes
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Artificial intelligence
  • Natural language interfaces
  • Speech recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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