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Reseach Article

Speaker Independent Recognition System with Mouse Movements

Published on February 2012 by R.L.K.Venkateswarlu, R. Vasantha Kumari, A.K.V.Nagayya
Optimization and On-chip Communication
Foundation of Computer Science USA
OOC - Number 1
February 2012
Authors: R.L.K.Venkateswarlu, R. Vasantha Kumari, A.K.V.Nagayya

R.L.K.Venkateswarlu, R. Vasantha Kumari, A.K.V.Nagayya . Speaker Independent Recognition System with Mouse Movements. Optimization and On-chip Communication. OOC, 1 (February 2012), 45-50.

author = { R.L.K.Venkateswarlu, R. Vasantha Kumari, A.K.V.Nagayya },
title = { Speaker Independent Recognition System with Mouse Movements },
journal = { Optimization and On-chip Communication },
issue_date = { February 2012 },
volume = { OOC },
number = { 1 },
month = { February },
year = { 2012 },
issn = 0975-8887,
pages = { 45-50 },
numpages = 6,
url = { /specialissues/ooc/number1/5471-1009/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Special Issue Article
%1 Optimization and On-chip Communication
%A R.L.K.Venkateswarlu
%A R. Vasantha Kumari
%A A.K.V.Nagayya
%T Speaker Independent Recognition System with Mouse Movements
%J Optimization and On-chip Communication
%@ 0975-8887
%N 1
%P 45-50
%D 2012
%I International Journal of Computer Applications

Speech recognition is potentially a multi-billion dollar industry in the near future. It is a natural alternative interface to computers for people with limited mobility in their arms and hands, sight, hearing limitation. For most current voice-mail systems, one has to follow series of touch-tone button presses to navigate through a hierarchical menu. Speech Recognition has the potential to cut through the menu hierarchy. Recently, neural networks have been considered for speech recognition tasks since in many cases they have shown comparable performance than the traditional approaches. There are two in-built threads in the recognition system. Thread 1 maintains the details about input acquisition where as thread 2 contains the classifier and decoder. The classifier used in this research is Radial Basis Function Neural Networks. The HMM graph is used as a decoder. The objective of the research is to make sure that the system is free from bugs. 100% accuracy is achieved by the recognition system.

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Index Terms

Computer Science
Information Sciences


Thread Recognizer Hidden Markov Model Radial Basis Function Mouse Movements