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Efficient ML training of CDHMM parameters based on prior evolution, posterior intervention and feedback
2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100)
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We present an efficient maximum likelihood (ML) training procedure for Gaussian mixture continuous density hidden Markov model (CDHMM) parameters. This procedure is proposed using the concept of approximate prior evolution, posterior intervention and feedback (PEPIF). In a series of experiments for training CDHMMs for a continuous Mandarin Chinese speech recognition task, the new PEPIF procedure achieves a 4-fold speed-up in terms of user CPU time over that of the Baum-Welch algorithm in
doi:10.1109/icassp.2000.859131
dblp:conf/icassp/HueSM00
fatcat:ryeaemcgnzgg7croqezkhbhxiu