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Polynomial segment models (PSMs), which are generalization of the hidden Markov models (HMMs), have opened an alternative research direction for speech recognition. However, they have been limited by their computational complexity. Traditionally, any change in PSM segment boundary requires likelihood recomputation of all the frames within the segment. This(More)
Polynomial Segment Model (PSM) has opened up an alternative research direction for acoustic modeling. In our previous papers [1, 2], we proposed efficient incremental likelihood evaluation and EM training algorithms for PSM, making it possible to train and recognize using PSM alone. In this paper, we shift our focus to make it feasible to use PSM on large(More)
One of the difficulties in using the polynomial segment model (PSM) to capture the temporal correlations within a phonetic segment is the lack of an efficient training algorithm comparable with the Baum-Welch algorithm in HMM. In our previous paper, we introduced a recursive likelihood computation algorithm for PSM recognition which can perform(More)
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