Реферат: Computer History Essay Research Paper ABSTRACTCurrent neural
at silent video stills of people saying each individual vowel, the network developed a series of
images of the different mouth, lip, teeth, and tongue positions. It then compared the video images
with the possible sound frequencies and guessed which combination was best.
Yuhas then combined the video recognition with the speech recognition systems and input a video frame
along with speech that had background noise. The system then estimated the possible sound frequencies
from the video and combined the estimates with the actual sound signals. After about 500 trial runs the
system was as proficient as a human looking at the same video sequences.
This combination of speech recognition and video imaging substantially increases the security factor by
not only recognizing a large vocabulary, but also by identifying the individual customer using the system.
? Current Applications
Laboratory advances like Ben Yuhas? have already created a steadily increasing market in speech recognition.
Speech recognition products are expected to break the billion-dollar sales mark this year for the first time.
Only three years ago, speech recognition products sold less than $200 million (Shaffer, 238).
Systems currently on the market include voice-activated dialing for cellular phones, made secure by their
recognition and authorization of a single approved caller. International telephone companies such as Sprint
are using similar voice recognition systems. Integrated Speech Solution in Massachusetts is investigating
speech applications which can take orders for mutual funds prospectuses and account activities (239).
? Optical Character Recognition
Another potential area for transaction security is in the identification of handwriting by optical
character recognition systems (OCR). In conventional OCR systems the program matches each letter in a
scanned document with a pre-arranged template stored in memory. Most OCR systems are designed specifically
for reading forms which are produced for that purpose. Other systems can achieve good results with
machine printed text in almost all font styles. However, none of the systems is capable of recognizing
handwritten characters. This is because every person writes differently.
Nestor, a company based in Providence, Rhode Island has developed handwriting recognition products based
on developments in neural network computers. Their system, NestorReader, recognizes handwritten characters
by extracting data sets, or feature vectors, from each character. The system processes the input
representations using a collection of three by three pixel edge templates (Pennisi, 23). The system then
lays a grid over the pixel array and pieces it together to form a letter. Then the network discovers