Combining Multiple Knowledge Sources for Continuous Speech Recognition

Abstract

The objective of this project has been to develop methods and techniques to coordinate the many sources of knowledge in the decision for a continuous speech recognition system. This effort includes finding methods for effectively combining information from various knowledge sources, and for developing recognition search strategies that find the most likely word sequence, given the input speech. These search strategies must consider a very large number of word-sequence hypotheses in a computationally efficient manner. To develop and demonstrate these techniques, we designed and implemented a complete word recognition system for continuous speech which is capable of incorporating knowledge from several sources, including lexical, phonetic, phonological, and grammatical knowledge. The complete system called BYBLOS, has been shown to achieve the highest recognition accuracy to date on standard government tests using a 1000-word continuous speech corpus.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1989
Accession Number
ADA212368

Entities

People

  • Alan Derr
  • C. Barry
  • O. Kimball
  • Robert E. Schwartz
  • Y-l. Chow

Organizations

  • BBN Technologies

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Human Systems

DTIC Thesaurus Topics

  • Acoustic Signals
  • Artificial Intelligence
  • Automated Speech Recognition
  • Computational Science
  • Context Free Grammars
  • Databases
  • Formal Languages
  • Grammars
  • Hidden Markov Models
  • Language
  • Markov Models
  • Probabilistic Models
  • Probability
  • Recognition
  • Resource Management
  • Standards
  • Test Sets

Readers

  • Computer Vision.
  • Organizational Process Management (OPM).
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation