Speech Recognition Software; An Alternative to Reduce Ship Control Manning

Abstract

This study identifies factors affecting the performance of commercial-off-the-shelf speech recognition software (SRS) when used for ship control purposes. After a review of research in the feasibility and acceptability of SRS-based ship control, the paper examines the effects of: * A restricted vocabulary versus a large vocabulary, * Low experience level conning officers versus high experience level conning officers, * Male versus female voices, * Pre-test training on specific words versus no pre-test training. Controlled experimentation finds that: * The experience level of a conning officer has no significant impact on SRS performance. * Female participants experienced more SRS errors than did their male counterparts. However, in this experiment, only a limited number of trials were available to assess a difference. * SRS with restricted vocabulary performs no better than SRS with large vocabularies. * Using the software "correct as you go" feature may impact software performance. Following the user profile establishment, individual user training on two specific words reduces error rates significantly.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA422331

Entities

People

  • Robert F. Kuffel

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Ambient Noise
  • Automated Speech Recognition
  • Background Noise
  • Computational Science
  • Hidden Markov Models
  • Information Science
  • Language
  • Mobile Phones
  • Naval Operations
  • Naval Vessels
  • Navy
  • Rhode Island
  • Simulators
  • Statistical Analysis
  • Students
  • Training
  • Virtual Reality

Readers

  • Instructional Design and Training Evaluation.
  • Naval Architecture and Marine Engineering.
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation