1. A method of determining an eigenspace for representing a plurality of training speakers, the method comprising the following steps: developing speaker-dependent sets of models for the individual training speakers while training speech data of the individual training speakers are used, the models (SD) of a set of models being described each time by a plurality of model parameters displaying a combined model for each speaker in a high-dimensional vector space (model space) by concatenation of a plurality of the model parameters of the models of the sets of models of the individual training speakers to a respective coherent superfactor performing a transformation to derive eigenspace basis vectors ( E e ), which utilizes a reduction criterion based on the variability of the vectors to be transformed, characterized in that the high-dimensional model space is in one step first reduced to a speaker subspace by a change of basis, in which speaker subspace all the training speakers are represented, an then, in a next step, inside this speaker subspace, the transformation is applied to the vectors representing the training speakers to obtain the eigenspace basis vectors ( E e )