This paper presents a stochastic segmental speech recogniser that models the a posteriori probabilities directly. The main issues concerning the system are segmental phoneme classification, utterance-level aggregation and the pruning of the search space. For phoneme classification, artificial neural networks and support vector machines are applied. Phonemic segmentation and utterance-level aggregation is performed with the aid of anti-phoneme modelling. At the phoneme level, the system convincingly outperforms the HMM system trained on the same corpus, while at the word level it attains the performance of the HMM system trained without embedded training.