Over two hundred years ago a Russian Professor Christian Kratzenstein explained physiological differences between five long vowels (/a/, /e/, /i/, /o/, and /u/) and made apparatus to produce them artificially. He constructed acoustic resonators similar to the human vocal tract and activated the resonators with vibrating reeds like in music instruments. (www.acoustics.hut.fi/publications/files/theses/lemmetty_ mst/chap2.html) The development of voice recognition continued until Alexander Graham Bell discovered how to convert air pressure waves (sound) into electrical impulses in 1870. In the 1950s, Bell Laboratories developed the first effective speech recognizer for numbers. By the 1980s, two distinct types of commercial products were available. The first offered speaker-independent recognition of small vocabularies. It was most useful for telephone transaction processing. The second, offered by Kurzweil Applied Intelligence, Dragon Systems, and IBM, focused on the development of large-vocabulary voice recognition systems so that text documents could be created by voice dictation. (www.allbusiness.com/ management/3603917-2.html) Modern voice recognition technologies involve quite complicated and sophisticated methods and algorithms. One of the methods applied recently in speech synthesis is hidden Markov models (HMM). The reason why HMMs are used in speech recognition is that a speech signal could be viewed as a piece-wise stationary signal or a short-time stationary signal and because they can be trained automatically, simple and computationally feasible to use. (www.wikipedia.com)
Recent technological advances have made recognition of more complex speech patterns possible. The availability of fast computers with inexpensive mass storage capabilities has enabled researchers to run many large scale experiments in a short amount of time. In fact, speech recognition systems with reasonable performance can now run in real time using high-end workstations without additional hardware.
Voice recognition technologies can now be applied on any particular real world-applications like texting in cellular phones. Since, it is used to automate many tasks that previously required hands-on human interaction. It can also serve as a data entry, command & control and also for document preparation.
This research is about integrating voice recognition on cellular phones. People dont have to use their thumbs to text because texting will be in a form of dictation. This means that, the person will dictate his/her message and the phone will automatically convert it into text. This is a challenging and worthwhile research that can be rewarding in many ways.
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