8. The method as claimed in claim 7, wherein the method further comprises calculating the modified local emission probability, for generating a feature vector by a state of a hidden Markov model, by calculating a logarithmic emission probability for each component of this feature vector and multiplying the logarithmic emission probability by a weighting, and by summing these weighted logarithmic emission probabilities over all components of this feature vector; the weighting of a component of a feature vector being in this case a measure of the reliability of this component on an assumption that only two specific states of a hidden Markov model are to be compared with one another.
8. The method as claimed in claim 7, wherein the method further comprises calculating the modified local emission probability, for generating a feature vector by a state of a hidden Markov model, by calculating a logarithmic emission probability for each component of this feature vector and multiplying the logarithmic emission probability by a weighting, and by summing these weighted logarithmic emission probabilities over all components of this feature vector; the weighting of a component of a feature vector being in this case a measure of the reliability of this component on an assumption that only two specific states of a hidden Markov model are to be compared with one another. 9. The method as claimed in claim 8, wherein the weightings are determined using a statistic quality measure for the reliability of a feature vector component.