1. A speech recognition method, comprising: receiving a speech input signal in a first noise environment which comprises a sequence of observations; determining the likelihood of a sequence of words arising from the sequence of observations using an acoustic model, comprising, providing an acoustic model for performing speech recognition on a input signal which comprises a sequence of observations, wherein said model has been trained to recognise speech in a second noise environment, said model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to an observation, and adapting the model trained in the second environment to that of the first environment; the speech recognition method further comprising, determining the likelihood of a sequence of observations occurring in a given language using a language model; and combining the likelihoods determined by the acoustic model and the language model and outputting a sequence of words identified from said speech input signal, wherein adapting the model trained in the second environment to that of the first environment comprises using second order or higher order Taylor expansion coefficients derived for a group of probability distributions and wherein the same expansion coefficient is used for the whole group; the speech recognition method further comprising estimating noise parameters used to determine the Taylor expansion coefficients, wherein the noise parameters comprise a component for additive noise and a component for convolutional noise, and an observation in the first noise environment is related to an observation in the second noise environment by: 
 y=x+h+g ( x, n, h )= x+h+C ln(1+ e C −1 (n−x−h) )  (1) where y is the observation in the first noise environment, x is the observation in the second noise environment, n is the additive noise, h is the convolutional noise in the first environment with respect to the second environment and C is the discrete cosine transformation matrix.