Accent phrase segmentation using transition probabilities between pitch pattern templates.
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This paper proposes a novel method for segmenting continuous speech into accent phrases by using a prosodic feature 'pitch pattern'. The pitch pattern extracted from input speech signals is divided into the accent segments automatically by using the One-Stage DP algorithm, in which reference templates representing various types of accent patterns and connectivity between them are used to find out the optimum sequence of accent segments. In case of making the reference templates from a large number of training data, the LBG clustering algorithm is used to represent typical accent patterns by a small number of templates. Evaluation tests were carried out using the ATR continuous speech database of a male speaker. Experimental results showed more than 91 boundaries were correctly detected.