SPECIALIST EQUIPMENT
THE NEW AI FRONTIER
Professor Jean S Verhardt explores how AI-driven voice technology can help prevent accidents in industry
The technology uses voice-based AI to predict fatigue with up to 90% accuracy I n his recent W all Street Journal
bestseller ‘The Sound of the Future: The Coming Age of Voice Technology’ (2023),
author Tobias Dengel shows us how artifi cial intelligence (AI)-driven voice technology (voice tech) is on the verge of transforming the world we live in. No part of society and no corner of industry will be left untouched by the voice revolution. What we have already seen with voice assistants such as Siri, Alexa and Google Assistant are just the fi rst gusts of a coming storm. Together with the mega voice
assistants from Silicon Valley, WOMBATT-Voz is one of the early practical applications of the marriage
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between artifi cial intelligence and voice technology. The objective for WOMBATT, in line with non-profi t campaign Vision Zero, is to completely eliminate fatigue and sleep related accidents both on the road and in industry. Today, fatigue is the single biggest cause of accidents in mining and accounts for over 20% of all fatal accidents on the roads and highways of the world – an unacceptable 200,000 fatigue-related road fatalities each year.
THE POWER OF VOICE TECHNOLOGY A growing number of our daily activities involving control of the machines that are an everyday part
of our lives, are already performed by auditory voice communications. A good example is cars and trucks. It is now possible using voice AI to verbally ask your car “Please check to see if I have left the iron on at home, and if so, turn it off ”. The car will reply “Very well” and then check via the internet connected house computer whether the iron is on, and if so, turn it off . By the same token, using the new WOMBATT-Voz voice-based AI fatigue prediction technology installed as an app on your mobile phone and connected to the car’s AI system, your voice can be analysed to detect early signs of fatigue, predicting your actual fatigue risk level up to fi ve hours ahead with more than 90% accuracy.
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