1. A method of adapting a language model with language model values for automatic speech recognition, in which test values are derived from a speech signal and compared with reference values determining a given vocabulary, there being derived scores which are linked to language model values at word boundaries, the language model values being dependent on the probability of occurrence of a given word of the vocabulary in dependence on at least one predecessor word, said method including the following steps: determination of a basic language model with basic language model values on the basis of training speech signals, determination, utilizing statistic calculation methods, of confidence intervals, having an upper and a lower boundary for language model values, on the basis of a different speech signal which deviates from the training speech signals, determination of a scaling factor in such a manner that the basic language model values scaled thereby satisfy an optimization criterion which the position of the scaled language model values are within the confidence intervals, use of scaled language model values which are situated in the confidence intervals and, in the case of scaled language model values situated beyond the boundaries of the confidence intervals, the nearest boundary as adapted language model values and, for confidence intervals not determined from the different speech signal, the basic language model values for the further recognition of the different speech signal.