Improving short-time speech frame recognition results by using context

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 classii-cations of nearby frames, also individually classiied. We investigate whether this improvement has an eeect to the accuracy of transcribing speech into phoneme sequences using two diierent 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 signiicantly by taking the local context into account.