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Automatic phone segmentation techniques based on model selection criteria are studied. We 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. We also demonstrate further improvement in phonemic segmentation by taking into account how the model parameters are related in the probability density functions of the underlying hypotheses as well as in the model selection via the information complexity criterion and by employing M-estimators of the model parameters. The proposed DISTBIC variants are tested on the NTIMIT database and the achieved F 1 measure is 74.7% using a 20-ms tolerance in phonemic segmentation.