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Animalese text to speech The ability of machines to convert human language into machine readable code. The idea is that it wouldn't be hard for computers to translate written or spoken words into machine-readable code which would then allow them to understand the message much better than humans could. One possible implementation might be a smartphone app that can display text in languages other than English, thus creating an interpreter that goes back and forth between human languages. However, there are still unresolved questions about how well current algorithms work for this purpose. The technology already exists to translate text into machine-readable code, but it is limited in terms of how many languages it can do that with. The most successful example of this is Google Translate, which translates the majority of languages into English with decent accuracy. There are some applications that work differently than Google Translate. For example, Microsoft's Word Flow translates text by using its Optical Character Reader (OCR). A Microsoft representative has stated that Word Flow is able to translate Chinese, French, Italian and Spanish with over 90% accuracy. Human language processing technologies may be used to improve translation for people who are hearing impaired or illiterate. There are various reasons why machine translation is not yet accurate. One problem is that the mathematics of machine translation is based on statistical models rather than on theoretical computer science. This means that it can make mistakes in recognizing words or phrases by using words that have similar sounds but have different meanings. For example, "quiz" and "quiz" are very close in sound, but have unrelated meanings. Another disadvantage of statistical models is that they work best for short texts. Another problem with machine translation algorithms is that they need to be trained for each language pair or else have no way of knowing how correct or inaccurate the results would be for new texts. This is because all of the words can be translated into a string of 0's and 1's. In order for a machine translation program to have any information it needs to have been trained with texts from both languages. For example, if a computer translates a German text with an English text, it would not know what word is wrong. This problem with machine translation is known as the "chicken or egg" problem. This means that there is no solution until one of the problems is solved first. There are various ways that machine translation has been used in real life. In the United States, NASA's NASA-sponsored Institute for the Learning Sciences developed a series of tools to assist people with disabilities in their daily lives. Examples include a "word wall" where any sign language word can be entered and a voice synthesizer that reads the words in an accompanying text. NASA also developed a "speech to text" tool that converts a sign language subscriber into written words. In the United Kingdom, the British Sign Language Education Center at Sheffield Hallam University has developed an online software platform called MindTwo for deaf students that is able to convert spoken English into written English and vice versa. cfa1e77820
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