In this paper we focus on the
part of segment-based speech recognition, where we have to distinguish the real phonemes from anything else which may appear (like parts of phonemes, several consecutive phonemes and noise). As it has to be performed while only having samples of the correct phonemes, it is an example of one-class classification. To solve this problem, first all phonemes are modelled with a number of Gaussian distributions; then the problem is converted into a two-class classification task by generating counter-examples; this way some machine learning algorithm (like ANNs) can be used to separate the two classes. We tested two methods for a counter-example generation like this: one was a solution specific to the anti-phoneme problem, while the other used a general algorithm. By making modifications to the latter to reduce its time requirements, we were able to achieve an improvement in the recognition scores of over 60% compared to having no anti-phoneme model at all, and it performed considerably better than the other two methods.