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