Patent ID: 6915260

Claim:
A method of determining an eigenspace for representing a plurality of training speakers, the method comprising the steps of: 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 sets of models of the individual training speakers to a respective coherent supervector; performing a transformation of the supervector to derive eigenspace basis vectors ( E c ),said transformation utilizing a reduction criterion based on the variability of the supervectors 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, and 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 c ) wherein the basis (Si,Sj) of the speaker subspace is spread from orthogonalized supervectors of the sets of models or from orthogonalized difference vectors of the supervectors of the sets of models to a selected origin and the origin of the basis (Si,Sj) of the sneaker subspace is selected such that the mean value of all the training speakers lies in the origin.