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Signal-to-string conversion based on high likelihood regions using embedded dynamic programming

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2 Author(s)
Y. Gong ; CRIN/INRIA-Lorraine, Vandoevre, France ; J. -P. Haton

A method of signal-to-string conversion based on embedded dynamic programming (DP) which can adapt its search to the variation of the input signal is proposed. The optimizing process is guided by high-valued portions of the likelihood function of symbols composing the string and is solved by two embedded dynamic programming processes. Algorithms in a Pascal-like language relating to the solution are given. When applied to continuous speech recognition on a 100-word vocabulary using the phoneme as the basic recognition unit, the method is shown to achieve a 4% improvement in the recognition rate compared to a classical DP-based method

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:13 ,  Issue: 3 )