Absstroct-This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class. HE problem of enhancing speech degraded by uncorrelated additive noise, when the noisy speech alone is available, has recently received much attention. This is due to the many potential applications a successful speech enhancement system can have, and because of the available technology which enables the implementation of such intricate algorithms. A comprehensive review of the various speech enhancement systems which emerged in recent years, and their classification according to the aspects of speech production and perception which they capitalize on, can be found in [ 11 . We focus here on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception [ l ] , [2]. In these systems the STSA of the speech signal is estimated, and combined with the short-time phase of the degraded speech, for constructing the enhanced signal. The "spectral subtraction" algorithm and Wiener filtering are wellknown examples [l] , [3]. In the "spectral subtraction" algorithm, the STSA is estimated as the square root of the maximum likelihood (ML) estimator of each signal spectral component variance [3] . In systems which are based on  Y. Ephraim was with the Technion
The MMSE STSA estimator derived by Ephraim and Malah REF is another noise reduction approach.
Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator