Segmenting speech using dynamic programming.

Abstract

Speech is modeled as a Markov chain. Scoring is developed to convert observations of the speech signal into estimated probabilities of the locations of segment boundaries. Dynamic programming is then used to compute a most-probable segmentation for the speech. The process automatically adjusts to speakers and incorporates a priori information in a probabilistic and systemic fashion. The performance of the algorithm appears to be state-of-the-art, independent of speaker.