Abstract: This voice recognition method comprises a decoding stage during which an enunciated word is identified on the basis of voice signal models described with the aid of voice units, each voice signal model representing a word belonging to a predefined vocabulary, and also comprises organizing voice signal models into an optimized lexical network associated with syntactic rules during which each word is identified with a word marker, wherein temporal information is inserted within the optimized lexical network in the form of additional generic markers, so as to spot relevant moments during the decoding.

Abstract: Input data are translated into a lexical output sequence. Sub-lexical entities and various possible combinations of the entities are identified as states ei and ej of first and second language models, respectively, intended to be stored, with an associated likelihood value and a table having memory areas. Each memory area is intended to contact at least one combination of the states and has an address equal to a value h [(ei:ej)] of a scalar function h applied to parameters peculiar to the combination (ei:ej). There is reduced complexity of accesses to information produced by a single transducer formed by a single Viterbi machine using the models.