Signal-to-String Conversion Based on High Likelihood Regions Using Embedded Dynamic Programming
Identifieur interne : 00D809 ( Main/Exploration ); précédent : 00D808; suivant : 00D810Signal-to-String Conversion Based on High Likelihood Regions Using Embedded Dynamic Programming
Auteurs : Y. Gong ; Jean-Paul Haton [France]Source :
- IEEE Transactions on Pattern Analysis and Machine Intelligence ; 1991.
English descriptors
Abstract
Many pattern recognition problems involve signal-to-string conversion. The string recognition problem can be formulated as the maximization of a constrained time integral of a sequence of likelihood functions. Such likelihood functions are time series of likelihood ratios between image of component symbols and input data. We propose in this paper a new method of conversion based on embedded dynamic programming which can adapt its search to the variation of the input signal. The optimizing process is guided by high-valued portions of likelihood function of symbols composing the string and is solved by two embedded dynamic programming process. Applied to continuous speech recognition using phoneme as basic recognition unit on a 100-word vocabulary, the method achieved 4========percnt; improvement of recognition rate in a 1/20 time compared to a classical DP-based method.
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Centre national de la recherche scientifique, Institut national de recherche en informatique et en automatique, Laboratoire lorrain de recherche en informatique et ses applications, Université de Lorraine
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