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HMM topology for boundary refinement in automatic speech segmentation

A boundary refinement method using a new hidden Markov model (HMM) topology is proposed for automatic phonetic speech segmentation. The proposed method has the ability to work at high frame rates and the training and boundary refinement stages are easy and fast. The method is data driven and can be adapted to any speech segmentation problem provided that a training set is available. Given an initial segmentation obtained by forced alignment using an HMM based phone recogniser, 20% decrease in boundary errors is achieved.