1. A speech recognition method implemented in a computer, comprising: receiving a speech input 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; 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; 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: adapting the model parameters of the model trained in the second noise environment to those of the first noise environment using transform parameters to produce a target distribution, wherein the transform parameters have a block diagonal form and are regression class dependent, each regression class comprising a plurality of probability distributions; mimicking the target distribution using a linear regression type distribution, said linear regression type distribution comprising mimicked transform parameters; and estimating the mimicked transformed parameters.
1. A speech recognition method implemented in a computer, comprising: receiving a speech input 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; 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; 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: adapting the model parameters of the model trained in the second noise environment to those of the first noise environment using transform parameters to produce a target distribution, wherein the transform parameters have a block diagonal form and are regression class dependent, each regression class comprising a plurality of probability distributions; mimicking the target distribution using a linear regression type distribution, said linear regression type distribution comprising mimicked transform parameters; and estimating the mimicked transformed parameters. 19. A non-transient carrier medium carrying computer readable instructions for controlling the computer to carry out the method of claim 1 .