Customer center

We are a boutique essay service, not a mass production custom writing factory. Let us create a perfect paper for you today!

Example research essay topic: Neural Networks Speech Recognition - 1,344 words

NOTE: Free essay sample provided on this page should be used for references or sample purposes only. The sample essay is available to anyone, so any direct quoting without mentioning the source will be considered plagiarism by schools, colleges and universities that use plagiarism detection software. To get a completely brand-new, plagiarism-free essay, please use our essay writing service.
One click instant price quote

... in more complex shapes, e. g. , a person's face, the professors estimate the requirement would be at least 100 times more pixels as well as additional circuits that mimic the movement-sensitive and edge-enhancing functions of the eye. They feel it is possible to achieve this number of pixels in the near future. When it does arrive, the new technology will likely be capable of recognizing human faces. Visual recognition would have an undeniable effect on reducing crime in automated financial transactions.

Future technology breakthroughs will bring visual recognition closer to the recognition of individuals, thereby enhancing the security of automated financial transactions. Voice recognition is another area that has been the subject of neural network research. Researchers have long been interested in developing an accurate computer-based system capable of understanding human speech as well as accurately identifying one speaker from another. Ben Yuhas, a computer engineer at John Hopkins University, has developed a promising system for understanding speech and identifying voices that utilizes the power of neural networks.

Previous attempts at this task have yielded systems that are capable of recognizing up to 10, 000 words, but only when each word is spoken slowly in an otherwise silent setting. This type of system is easily confused by back Ben Yuhas' theory is based on the notion that understanding human speech is aided, to some small degree, by reading lips while trying to listen. The emphasis on lip reading is thought to increase as the surrounding noise levels increase. This theory has been applied to speech recognition by adding a system that allows the computer to view the speaker's lips through a video analysis system while The computer, through the neural network, can learn from its mistakes through a training session. Looking 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. 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). 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, Nestor Reader, 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 (Penis, 23).

The system then lays a grid over the pixel array and pieces it together to form a letter. Then the network discovers which letter the feature vector most closely matched. The system can learn through trial and error, and it has an accuracy of about 80 percent. Eventually this system will be able to evaluate all symbols It is possible to implement new neural-network based OCR systems into standard large optical systems.

Those older systems, used for automated processing of forms and documents, are limited to reading typed block letters. When added to these systems, neural networks improve accuracy of reading not only typed letters but also handwritten characters. Along with automated form processing, neural networks will analyze signatures for possible forgeries. Neural networks are still considered emerging technology and have a long way to go toward achieving their goals. This is certainly true for financial transaction security.

But with the current capabilities, neural networks can certainly assist humans in complex tasks where large amounts of data need to be analyzed. For visual recognition of individual customers, neural networks are still in the simple pattern matching stages and will need more development before commercially acceptable products are available. Speech recognition, on the other hand, is already a huge industry with customers ranging from individual computer users to international telephone companies. For security, voice recognition could be an added link to the chain of pre-established systems. For example, automated account inquiry, by telephone, is a popular method for customers to determine the status of existing accounts. With voice identification of customers, an option could be added for a customer to request account transactions and payments to other institutions.

For credit card fraud detection, banks have relied on computers to identify suspicious transactions. In fraud detection, these programs look for sudden changes in spending patterns such as large cash withdrawals or erratic spending. The drawback to this approach is that there are more accounts flagged for possible fraud than there are investigators. The number of flags could be dramatically reduced with optical character recognition to help focus investigative efforts. It is expected that the upcoming neural network chips and add-on boards from Intel will add blinding speed to the current network software.

These systems will even further reduce losses due to fraud by enabling more data to be processed more quickly and with greater accuracy. Breakthroughs in neural network technology have already created many new applications in financial transaction security. Currently, neural network applications focus on processing data such as loan applications, and flagging possible loan risks. As computer hardware speed increases and as neural networks get smarter, "real-time" neural network applications should become a reality. "Real-time" processing means the network processes the transactions as they occur. 1.

Watch for advances in visual recognition hardware / neural networks. When available, commercially produced visual recognition systems will greatly enhance the security of automated financial transactions. 2. Computer aided voice recognition is already a reality. This technology should be implemented in automated telephone account inquiries. The feasibility of adding phone transactions should also be considered. Cooperation among financial institutions could result in secure transfers of funds between banks when ordered by the customers over the telephone. 3.

Handwriting recognition by OCR systems should be combined with existing check processing systems. These systems can reject checks that are possible forgeries. Investigators could follow-up on the OCR rejection by making appropriate inquiries with the check writer. Bibliography: Winston, Patrick.

Artificial Intelligence. Menlo Park: Addison-Wesley Publishing, 1988. Welstead, Stephen. Neural Network and Fuzzy Logic in C/C++. New York: Welstead, 1994. Brody, Herb. "Computers That Learn by Doing. " Technology Review August 1990: 42 - 49.

Thompson, William. "Overturning the Category Bucket. " BYTE January 1991: 249 - 50 +. Hinton, Geoffrey. "How Neural Networks Learn from Experience. " Scientific American September 1992: 145 - 151. Dreyfus, Hubert. , and Stuart E. Dreyfus. "Why Computers May Never Think Like People. " Technology Review January 1986: 42 - 61. Shaffer, Richard. "Computers with Ears. " FORBES September 1994: 238 - 239.


Free research essays on topics related to: speech recognition, financial transactions, voice recognition, neural networks, telephone companies

Research essay sample on Neural Networks Speech Recognition

Writing service prices per page

  • $18.85 - in 14 days
  • $19.95 - in 3 days
  • $23.95 - within 48 hours
  • $26.95 - within 24 hours
  • $29.95 - within 12 hours
  • $34.95 - within 6 hours
  • $39.95 - within 3 hours
  • Calculate total price

Our guarantee

  • 100% money back guarantee
  • plagiarism-free authentic works
  • completely confidential service
  • timely revisions until completely satisfied
  • 24/7 customer support
  • payments protected by PayPal

Secure payment

With EssayChief you get

  • Strict plagiarism detection regulations
  • 300+ words per page
  • Times New Roman font 12 pts, double-spaced
  • FREE abstract, outline, bibliography
  • Money back guarantee for missed deadline
  • Round-the-clock customer support
  • Complete anonymity of all our clients
  • Custom essays
  • Writing service

EssayChief can handle your

  • essays, term papers
  • book and movie reports
  • Power Point presentations
  • annotated bibliographies
  • theses, dissertations
  • exam preparations
  • editing and proofreading of your texts
  • academic ghostwriting of any kind

Free essay samples

Browse essays by topic:

Stay with EssayChief! We offer 10% discount to all our return customers. Once you place your order you will receive an email with the password. You can use this password for unlimited period and you can share it with your friends!

Academic ghostwriting

About us

© 2002-2024 EssayChief.com