  Form about four decades human beings have been dreaming of an intelligent
machine which can master the natural speech. In its simplest form, this machine
should consist of two subsystems, namely automatic speech recognition (ASR) and
speech understanding (SU). The goal of ASR is to transcribe natural speech
while SU is to understand the meaning of the transcription. Recognizing and
understanding a spoken sentence is obviously a knowledge-intensive process,
which must take into account all variable information about the speech
communication process, from acoustics to semantics and pragmatics. While
developing an Automatic Speech Recognition System, it is observed that some
adverse conditions degrade the performance of the Speech Recognition System. In
this contribution, speech enhancement system is introduced for enhancing speech
signals corrupted by additive noise and improving the performance of Automatic
Speech Recognizers in noisy conditions. Automatic speech recognition
experiments show that replacing noisy speech signals by the corresponding
enhanced speech signals leads to an improvement in the recognition accuracies.
The amount of improvement varies with the type of the corrupting noise.
