In this paper we present an algorithm for segmenting or locating the endpoints of speech in a continuous signal stream. The proposed algorithm is based on non-linear likelihood-based projections derived from a Bayesian classifier. It utilizes class distributions in a speech/non-speech classifier to project the signal into a 2-dimensional space where, in the ideal case, optimal classification can be performed with a simple linear discriminant. The projection results in the transformation of diffuse, nebulous classes in high-dimensional space into compact clusters in the low-dimensional space that can be easily separated by simple clustering mechanisms. In this space, decision boundaries for optimal classification can be more easily identified using simple clustering criteria. The segmentation algorithm proposed utilizes this property to determine and update optimal classification thresholds continuously for the signal being segmented. The performance of the proposed algorithm has been evaluated on data recorded under extremely diverse environmental noise conditions. The experiments show that the algorithm performs comparably to manual segmentations even under these diverse conditions.