Using commercial-off-the-shelf speech recognition software for conning U.S. warships

Abstract

The U.S. Navy's Transformation Roadmap is leading the fleet in a smaller, faster, and more technologically advanced direction. Smaller platforms and reduced manpower resources create opportunities to fill important positions, including ship-handling control, with technology. This thesis investigates the feasibility of using commercial-off-the-shelf (COTS) speech recognition software (SRS) for conning a Navy ship. Dragon NaturallySpeaking Version 6.0 software and a SHURE wireless microphone were selected for this study. An experiment, with a limited number of subjects, was conducted at the Marine Safety International, San Diego, California ship-handling simulation facility. It measured the software error rate during conning operations. Data analysis sought to determine the types and significant causes of error. Analysis includes factors such as iteration number, subject, scenario, setting and ambient noise. Their significance provides key insights for future experimentation. The selected COTS technology for this study proved promising overcoming irregularities particular to conning, but the software vocabulary and grammar were problematic. The use of SRS for conning ships merits additional research, using a limited lexicon and a modified grammar which supports conning commands. Cooperative research between the Navy and industry could produce the "Helmsman" of the future.