In this work, we compare different approaches for speech segmentation, of which some are constrained and the remaining are unconstrained by phone transcript. A high accuracy speech segmentation can be obtained by approaches constrained by phone transcript such as HMM forced-alignment when {it exact phone transcript} is known. But such approaches have to adjust with {it canonical phone transcript}, as {it exact phone transcript} is tough to obtain. Our experiments on TIMIT corpus demonstrate that ANN and HMM phone-loop based unconstrained approaches, perform better than HMM forced-alignment based approach constrained by {it canonical phone transcript}. Finally a detailed error analysis of these approaches is reported.