This paper describes a approach to speech segmentation. Unlike approaches based on spectral measurements, our algorithm iteratively clusters on an LPC representation of time waveform blocks. The algorithm uses a generalized maximum likelihood criterion for deciding when two neighboring pieces of the signal should be joined. This paper describes the algorithm and shows that it yields superior results when compared to metrics based on spectral or cepstral measurements.