ADAPTIVE SPEECH ENHANCEMENT TECHNIQUES FOR COMPUTER BASED SPEAKER RECOGNITION

摘要

Extraction of high resolution speech signals is important task in all practical applications. During the transmission of desired signals many noises are contaminated. The Least Mean Square (LMS) algorithm is a basic adaptive algorithm has been widely used in many applications as a significance of its simplicity and robustness. In practical application of the LMS algorithm, an important parameter is the step size. It is well known that if the convergence rate of the LMS algorithm will be rapid for the step size is fast, but the drawback is steady-state mean square error (MSE) will raise. On the other side, for the small step size, the steady state MSE will be small, but the convergence rate will be slow. Thus, the step size provides a tradeoff between the convergence rate and the steady-state MSE of the LMS algorithm. Make the step size variable rather than fixed to enhance the performance of the LMS algorithm, that is, choose large step size values during the initial convergence of the LMS algorithm, and use small step size values when the system is close to its steady state, which results in Normalized LMS (NLMS) algorithms. In this technique the step size is not constant and varies according to the error signal at that instant. In order to improve the quality of the speech signal, decrease the mean square error and increasing signal to noise ratio of the filtered signal, Weight Normalized LMS(WNLMS), Error Normalized LMS(ENLMS), Unbiased LMS (UBLMS) algorithms are being introduced as quality factor. These Adaptive noise cancellers are compared with respect to Signal to Noise Ratio Improvement (SNRI).