The software part consists of managing the windowing block. The hardware part consists of the autocorrelation core that solves the part of the algorithm with higher computational costs. For the developing geographic the where the traffic is non-lane driver and other technique (magnetic loop detector) are inapplicable. Adaptive classifiers are used to model the traffic density state as low, medium and heavy. Two conclusions can be drawn from the results, the first, MFCC has better performance with long recording, LPC residual has better performance with short recording. We will estimate three probable conditions of traffic that is heavy flow \, medium flow and low flow (free flow).We will use non contemporaneous recording and scarcity of data for training and testing the performance of recognition task. In this project we will estimate the vehicular traffic density by using three class neural network classifiers. Wiener filter algorithm scored accuracy of 100%, 5%, and 50% for test cases i,ii,and iii respectively, with recognition speed range of (695-867) msec and estimated power range of (750-885) µW. From the simulation results, the Wiener Filter algorithm outperform the other four algorithms in terms of all measure of performance, and power requirement with the moderate complexity of the algorithm and its prospective implementation as a hardware. Ten words were spoken in an isolated way by male and female speakers (four speakers) using MATLAB as a simulation environment, these word were used as a reference signal to trained the algorithm, for evaluating phase, all algorithms dictates to subject them to similar test criteria. Also, to implement and verify the chosen voice recognition algorithm using MATLAB. This research were proposed to review several voice algorithms in terms of detection accuracy and processing overhead and to identify the optimal voice recognition algorithm that can give the best trade-offs between processing cost (speed, power) and accuracy. Voice recognition system performance is commonly specified in terms of speed and accuracy, recognition accuracy is the most important and straightforward measure of voice recognition performance.
The past decade has seen dramatic progress in voice recognition technology, to the extent that systems and high-performance algorithms have become accessible. Voice recognition has become one of the most important tools of the modern generation and is widely used in various fields for various purposes.
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