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<p>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.</p>
high likelihood regions; embedded dynamic programming; signal-to-string conversion; search; Pascal-like language; continuous speech recognition; 100-word vocabulary; dynamic programming; pattern recognition; search problems; speech recognition

J. Haton and Y. Gong, "Signal-to-String Conversion Based on High Likelihood Regions Using Embedded Dynamic Programming," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. , pp. 297-302, 1991.
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