Language Learning


SignAll is slowly but surely building a sign language translation platform

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Translating is difficult work, the more so the further two languages are from one another. But sign language is a unique case, and translating it uniquely difficult, because it is fundamentally different from spoken and written languages. All the same, SignAll has been working hard for years to make accurate, real-time machine translation of ASL a reality.


How Will Machine Translators Change Language Learning?

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The code has been copied to your clipboard. Some machines can take something written in one language and give users the same or similar wording in another language. These machines are designed to do this kind of work quickly and without mistakes. Some of the devices are so small they can be carried around the world. The quality of translation software programs has greatly improved in recent years, thanks to new, fast-developing technologies.


Automatic sign language translators turn signing into text

New Scientist

Machine translation systems that convert sign language into text and back again are helping people who are deaf or have difficulty hearing to communicate with those who cannot sign. KinTrans, a start-up based in Dallas, Texas, is trialling its technology in a bank and government offices in the United Arab Emirates, and plans to install it in more places over the next couple of months. SignAll, a company based in Budapest, Hungary, will begin its own trials next year. KinTrans uses a 3D camera to track the movement of a person's hands as they sign words. A sign language user can approach a bank teller and sign to the KinTrans camera that they'd like assistance, for example.


How Silicon Valley is teaching language to machines

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The dream of building computers or robots that communicate like humans has been with us for many decades now. And if market trends and investment levels are any guide, it's something we would really like to have. MarketsandMarkets says the natural language processing (NLP) industry will be worth $16.07 billion by 2021, growing at a rate of 16.1 percent, and deep learning is estimated to reach $1.7 billion by 2022, growing at a CAGR of 65.3 percent between 2016 and 2022. Of course, if you've played with any chatbots, you will know that it's a promise that is yet to be fulfilled. There's an "uncanny valley" where, at one end, we sense we're not talking to a real person and, at the other end, the machine just doesn't "get" what we mean.


IBM Research Demonstrates Innovative 'Speech to Sign Language' Translation System

AITopics Original Links

HURSLEY, UK--(Marketwire - September 13, 2007) - IBM (NYSE: IBM) has developed an ingenious system called SiSi (Say It Sign It) that automatically converts the spoken word into British Sign Language (BSL) which is then signed by an animated digital character or avatar. SiSi brings together a number of computer technologies. A speech recognition module converts the spoken word into text, which SiSi then interprets into gestures, that are used to animate an avatar which signs in BSL. Upon development this system would see a signing avatar'pop up' in the corner of the display screen in use -- whether that be a laptop, personal computer, TV, meeting-room display or auditorium screen. Users would be able select the size and appearance of the avatar.


Google Translate is tapping into neural networks for smarter language learning

PCWorld

Google Translate is rolling out a major upgrade that promises more human-like language translations. Google is bullish on its Neural Machine Translation technology, claiming that it's a bigger upgrade to the service than everything that's been accomplished in the last ten years combined. The company is rolling out the improvements to eight language pairs in Google search, the Translate apps, and the website. You'll find the new technology behind translations between English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish. Google says that makes up more than 35 percent of all language queries.


Towards Automatic Translation from Japanese into Japanese Sign Language

AAAI Conferences

TOKUDA Masaaki, OKUMURA Manabu School of Information Science, Japan Advanced Institute of Science and Technology (Tatsunokuchi, Ishikawa 923-12 Japan) tokuda@jaist.ac.jp, oku@jaist.ac.jp Abstract In this paper, we present a prototype MT system named SYUWAN which can translate Japanese into Japanese sign language. One of the most important problems in this translation is that there are very few entries in a sign language dictionary, compared with Japanese one. To solve this problem, when the original input word does not exist in a sign language dictionary, SYUWAN applies some techniques to find a similar word from a Japanese dictionary and substitutes this word for the original word. As the result, SYUWAN can translate up to 95% of words which are morphologically analyzed. Introduction The deaf communicate with each other by using sign language which is composed of hands, arms and face expression. Japanese deafs use Japanese sign language (JSL) which is different from both phonetic language (Japanese language) and other sign language (American sign language (ASL)). According to recent linguistic researches, JSL has peculiar syntax. However, there are few researches on JSL, and no practical machine translation (MT) system between sign language and phonetics language exists.