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

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