Communication within the maritime setting is necessary for security however doesn’t at all times run easily. This will have catastrophic penalties. In spite of everything, 90% of all accidents at sea are attributable to human error, and half of those by issues with communication. In collaboration with Jade University of Utilized Sciences (Elsfleth Campus) and companions from trade, Fraunhofer IDMT in Oldenburg, Germany, has been working for a while on enhancing safety-relevant voice communication, significantly at sea.
“In addition to developing chatbots for training purposes and recruitment procedures, the focus is on systems for monitoring voice commands on the ship’s bridge. For this purpose, we have trained our speech recognizer’s AI with special vocabulary, known as nautical terms and phrases, or maritime English,” says Jan Wellmann, head of automated speech recognition on the Oldenburg Department for Listening to, Speech and Audio Know-how HSA of Fraunhofer IDMT.
ELNAV, a start-up firm based mostly within the Croatian port metropolis of Break up, is growing what it calls a Helm Order Monitor. This digital gadget makes use of speech recognition technology from Fraunhofer IDMT in Oldenburg together with knowledge acquired from the ship’s sensors and displays whether or not the instructions issued are clear, confirmed and appropriately executed.
Hrvoje Mihovilović, ELNAV’s founder and CEO says, “In the beginning, the biggest obstacle for ELNAV was deciding whether to develop our own speech recognition system or find a partner. We needed a robust speech detector that would work reliably even under difficult acoustic conditions. We soon discovered that Fraunhofer IDMT was developing a speech recognition model for maritime communications and therefore an ideal partner for the development of our Helm Order Monitor.”
The companions confronted three primary technological challenges. The primary was the sign degradation brought on when utilizing far-field microphones in speech-processing purposes. The noise level on the bridge shouldn’t intrude with verbal communication, masks audible alarms or be uncomfortable for bridge personnel: the ambient noise degree on the bridge in calm climate shouldn’t exceed 65 dB(A). To unravel this drawback, microphone array sign processing is offered as a substitute.
The accuracy of automated speech recognition (ASR) beneath totally different noise situations and at numerous distances was an additional problem. To make the ASR system extra sturdy in noisy environments, deep neural networks (DNN) are used to reinforce speech.
A 3rd problem was catering for the limitless variations of the English language—from totally different regional accents to idiosyncratic use of grammar and vocabulary. Right here, machine learning is used to create a single, complete language pack which is correct and encompasses as many variations of English as attainable.
“The latest report on European Maritime Safety (EMSAFE) sees digitalization and increasing automation as a major opportunity. However, the technologies could also bring new challenges for safety as well as crew training. The changes required for this are supported by our speech recognition system. It can be used to learn, test and monitor maritime English in practice,” says Dr. Jens Appell, head of the Oldenburg department of Fraunhofer IDMT.
Higher security by higher communication at sea (2022, November 23)
retrieved 23 November 2022
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