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Hidden Markov models are very important for analysis of signals and systems. In the past two decades they have been attracting the attention of the speech processing community, and recently they have become the favorite models of biologists. Major weakness of conventional hidden Markov models is their inflexibility in modeling state duration. In this paper, we analyze nonstationary hidden Markov models whose state transition probabilities are functions of time, thereby indirectly modeling statedoi:10.1109/icassp.1999.756330 dblp:conf/icassp/DjuricC99 fatcat:dstbzcagnbdcleahhuhqf6brua