2. The method according to claim 1 , wherein each cluster includes at least one sub-cluster, and wherein the identified expression dependent weightings are retrieved for said each cluster such that there is one weight per sub-cluster.
1. A method of animating a computer generation of a face having a mouth, the method comprising: receiving a text input related to speech, which is to be output by movement of the mouth; dividing the text input into a sequence of acoustic units including one of at least phonemes, graphemes, and words or parts of words; analyzing the text input related to the speech to identify expression-dependent weightings related to a speech expression and a corresponding facial expression, to be input into a statistical model; converting the sequence of acoustic units into a sequence of image vectors and a sequence of speech vectors using the statistical model, wherein the model has a plurality of model parameters comprising mathematical means of probability distributions, which relate an acoustic unit in the sequence of acoustic units to are image vector in the sequence of image vectors and to a speech vector in the sequence of speech vectors, the image vector including a plurality of parameters that define the face; and outputting the sequence of image vectors and the sequence of speech vectors, wherein the sequence of image vectors are output as video such that the mouth moves to mime the speech expression associated with the corresponding facial expression, and wherein the sequence of speech vectors are output as audio, which is synchronized with lip movement of the mouth and is associated with the speech expression, wherein the mathematical means of each probability distribution of the probability distributions for the speech expression and the corresponding facial expression are expressed as a weighted sum of independent mathematical means, wherein weightings used in the weighted sum are the identified expression-dependent weightings, wherein the independent mathematical means are provided in clusters, and wherein here is one expression-dependent weighting per cluster.