Environmental noise has a negative impact on the accuracy of speech and sound recognition systems. Background noise corrupts acoustic features of sound. Because automatic recognition models are usually trained on a database composed of “clean” signals (no background noise), the decoding is biased when the signal is corrupted by additive noise and channel distortion. To overcome this problem, several techniques are proposed in the literature. One solution is to denoise the speech signal to be decoded before it is processed by the ASR. In practice, this is done by applying noise reduction techniques based on Wiener Filter or Ephraim-Malah Filter. Another solution is to train the ASR system under a variety of environmental conditions. However, this solution requires a large memory capacity to store all the noisy signals. Another idea is to estimate the “noisy” acoustic model (HMM or Hidden Markov Models) from the “clean” acoustic model. Two common techniques using this approach are the Parallel Model Combination (PMC)  and Vector Taylor Series (VTS) compensation, which will be presented here.