A Practical Methodology for the Evaluation of Spoken Language Systems

Abstract

A meaningful evaluation methodology can advance the state-of-the-art by encouraging mature, practical applications rather than "toy" implementations. Evaluation is also crucial to assessing competing claims and identifying promising technical approaches. While work in speech recognition (SR) has a history of evaluation methodologies that permit comparison among various systems, until recently no methodology existed for either developers of natural language (NL) interfaces or researchers in speech understanding (SU) to evaluate and compare the systems they developed. Recently considerable progress has been made by a number of groups involved in the DARPA Spoken Language Systems (SLS) program to agree on a methodology for comparative evaluation of SLS systems, and that methodology has been put into practice several times in comparative tests of several SLS systems. These evaluations are probably the only NL evaluations other than the series of Message Understanding Conferences (Sundheim, 1989; Sundheim, 1991) to have been developed and used by a group of researchers at different sites, although several excellent workshops have been held to study some of these problems (Palmer et al., 1989; Neal et a!., 1991).

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADA457494

Entities

People

  • Madeleine Bates
  • Sean Boisen

Organizations

  • BBN Technologies

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Agreements
  • Aircrafts
  • Automated Speech Recognition
  • Automatic
  • California
  • Databases
  • Decoding
  • Fish
  • Language
  • Natural Language Processing
  • Natural Languages
  • Real Numbers
  • Specifications
  • Standards
  • Test And Evaluation
  • Test Sets
  • United States

Fields of Study

  • Computer science

Readers

  • Academic Conference Management
  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation