Asking Questions to Limited Domain Virtual Characters: How Good Does Speech Recognition Have to Be?

Abstract

In this paper, we describe the evaluation of a limited domain question-answering characters, particularly as to the effect of non-optimal speech recognition, and the ability to appropriately answer novel questions. Results show that answering ability is robust until speech recognition reaches over 60% Word error rate.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
AD1157994

Entities

People

  • Anton Leuski
  • Brandon Kennedy
  • David R Traum
  • Ronakkumar Patel

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Applied Computer Science
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • California
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computer Programs
  • Dialogue Systems
  • Errors
  • Information Retrieval
  • Language
  • Linguistics
  • Machine Learning
  • Natural Languages
  • Probability
  • Probability Distributions
  • Recognition
  • Supervised Machine Learning
  • Training
  • United States

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks