1. A speech recognition method in which a basic set of models, which comprises models for various acoustic units, while the models are described by a plurality of model parameters, is adapted to a current speaker based on already observed speech data of this speaker, characterized in that the basic set of models is represented by a supervector in a high-dimensional vector space (model space), where the supervector is formed by concatenation of the plurality of model parameters of the models of the basic set of models, and this basic set of models in the model space is adapted to the speakers by means of a MAP method, while an asymmetrical distribution in the model space is chosen as an a priori distribution for the MAP method.