Additive and convolutional noises are the main problems to be solved in order to make speech recognition successful in real applications. A model for additive noise is used to deduce a spectral subtraction (SS) estimation and to show that the channel transfer function could be effectively removed alter the additive noise being cancelled by SS. Then, SS and mean normalization are tested in combination with a weighting procedure to reduce the influence ol the rectilying lunction. All the experiments were done in the context ol weighted matching algorithms and the approaches proved effective in cancelling both additive noise and the transmission channel function.