Phonemic segmentation of fluent speech

Abstract

A hierarchical approach to phonemic segmentation of continuous, speaker-independent speech is presented. Each sentence is divided into distinct obstruent and sonorant regions using a Bayesian decision surface. Rules are then used to make context specific corrections with these regions. Finally, finer segmentation is performed using a number of rulea specific to obstruent and sonorant boundaries. Around 80% of the boundaries are located with an insertion rate of 12%. The developed system is suitable for use in phoneme recognition and automatic labelling of speech.