This paper focuses on comparing three approaches to improve the accuracy of classifying short-time speech frames into phoneme classes by taking into account the classifications of nearby frames, also individually classified. We investigate whether this improvement has an effect to the accuracy of transcribing speech into phoneme sequences using two different decoding schemes, one based on simple durational rules, and the other on hidden Markov models (HMMs). The experiments indicate that recognition accuracies can indeed be improved significantly by taking the local context into account.