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Speech recognition in Java
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Similar to natural language processing is speech recognition Like NLP, speech recognition must identify a word and deduce its meaning But unlike NLP, speech recognition has the added burden of trying to do all this in real-time The moment someone says a word, the speech recognition computer must quickly understand that word because the speaker won t likely pause for long before saying the next word The simplest form of speech recognition involves choosing from a limited selection of distinctly different sounding words Many voicemail systems offer this feature by asking a question such as, Do you want to leave a message At this point, the speech recognition computer listens for any sound that resembles either Yes or No Because the speech recognition computer has such a limited selection to choose from, its accuracy rate can be almost perfect A second way to understand speech is to force users to train the computer first by saying a limited selection of words The computer then stores these spoken sounds in a database so the next time the user talks, the speech recognition matches every spoken word with the closest match in its database
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Such speech recognition systems are extremely accurate, but suffer both from the hassle of forcing users to train the computer for long periods of time and from the fact that after training the computer, it can still understand only a limited vocabulary Even worse is that accuracy among speech recognition systems degrades dramatically with background noise or if the user s own voice changes from the time of training to the time of actually using the speech recognition system For example, someone with a cold sounds different than normal, which might cause the speech recognition system to be unable to identify his spoken commands Noise in the background can often affect the speech recognition system s accuracy, which is why most speech recognition systems require the use of a microphone positioned directly in front of the user s mouth The ultimate goal of speech recognition systems is to recognize spoken language no matter who s speaking, what background noise there may be, or what he might actually be saying In other words, speech recognition systems strive for understanding spoken language as easily as people do
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Another form of pattern recognition involves recognizing images, such as faces in a picture or handwritten letters on a piece of paper Recognizing written characters is known as optical character recognition (OCR) and is commonly used in computer scanners OCR software studies an image of a character, and based on its knowledge of letters, the OCR program tries to match the written character with its database of known characters After OCR finds a match, it can decipher the contents of a written phrase letter by letter OCR programs have trouble identifying near-identical characters, such as the lowercase letter l and the number 1, or the letter O and the number zero, 0 That s why OCR scanning programs often insert incorrect characters in the middle of scanned text Image recognition is a much harder problem, but one that s commonly used in security systems, such as in casinos Every casino contains a photographic database of known cheaters If a casino suspects one of these known cheaters has entered its premises, security guards can zoom in on the suspect and use image recognition software to determine whether that person is actually a known cheater in the database
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Such image recognition software examines the shape of a person s face along with the physical position of the nose in relation to the eyebrows, lips, and chin No matter how many disguises cheaters may wear, they can t hide or change the physical dimensions of their faces, which is how such image recognition programs can spot them In the field of robotics, image processing is crucial because it gives robots the ability to sense their environment and avoid or move toward certain objects Teaching a robot to avoid objects in its path might seem easy until the robot looks through a plate glass window and mistakes the window for an opening that it can roll through to get to the other side Although image recognition might help a robot identify the edges of walls or the end of a road, image recognition must also teach a computer to recognize shadows or reflections that can distort images Primitive image recognition might simply distinguish between patches of light and dark, but more sophisticated image recognition could not only recognize an object in its path but also identify what that object might be, which involves a related category of artificial intelligence known as image processing In a limited domain of objects, seeing and understanding images can be determined in advance, but in the real world, the number of images a computer might see and need to recognize is nearly infinite To solve the problem of not only recognizing an image but also understanding what that image might be, computers need to learn independent of their human programmers
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