Pre-Processing of Speech Signal is very crucial in the applications where silence or background noise is completely undesirable. The degradation of speech due to the presence of noise causes severe difficulties in various communication environments. Therefore, in this paper it is needed to first apply a noise removal and silence removal methods, in order to detect” clean” speech segments. Then using that clean speech the compression process was performed using the most powerful compression technique such as Linear Predictive Coding (LPC). Here different samples of spoken words are collected from different speakers and are used for implementation. The samples which is denoised, silence removed and compressed were compared with the samples which is not denoised but silence removed and compressed using LPC. Finally the results were evaluated in terms of compressed ratio (CR), Peak signal-to-noise ratio (PSNR) and Normalized root-mean square error (NRMSE).Finally the result show that the samples which is denoised, silence removed and compressed gives better result than the other samples.