Natural Language Processing for Joint Fire Observer Training

Abstract

We describe recent research to enhance a training system which interprets Call for Fire (CFF) radio artillery requests. The research explores the feasibility of extending the system to also understand calls for Close Air Support (CAS). This work includes automated analysis of complex language behavior in CAS missions, evaluation of speech recognition performance, and simulation of speech recognition errors.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2010
Accession Number
ADA550370

Entities

People

  • Antonio Roque
  • David R Traum
  • Kallirroi Georgila
  • Kenji Sagae
  • Ron Artstein

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Automated Speech Recognition
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Data Sets
  • Dialogue Systems
  • Identification
  • Language
  • Military Training
  • Natural Language Processing
  • Natural Languages
  • Recognition
  • Simulations
  • Tactical Air Support
  • Training
  • Virtual Reality

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Science.
  • Military Training and Readiness Simulation

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Translation