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Automatic phone segmentation techniques based on model selection criteria are studied. The authors investigate the phone boundary detection efficiency of entropy- and Bayesian-based model selection criteria in continuous speech based on the DISTBIC hybrid segmentation algorithm. DISTBIC is a text-independent bottom-up approach that identifies sequential model changes by combining metric distances with statistical hypothesis testing. Using robust statistics and small sample corrections in the baseline DISTBIC algorithm, phone boundary detection accuracy is significantly improved, while false alarms are reduced.
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