Generative grammars and dynamic programming in speech recognition with learning

The grammars are proposed which generate all possible reference signals of speech using a finite number of elementary reference signals and a finite system of rules. These grammars take into account the essential factors of variability of speech signals: coarticulation of sounds, nonlinear change of the rate and the intensity of pronouncing. The recognition of a shown speech signal consists in finding among all reference signals one which has the greatest resemblance to the shown signal and indicating the sequence of phonemes, syllables or words that corresponds to this reference signal. The recognition problem is solved by using the dynamic programming method. The learning consists in evaluating the parameters of the grammars: alphabet of elementary reference signals, acoustic-phonetic transcriptions of words and so on. Some algorithms for speech recognition are proposed: recognition of words, recognition of connected speech composed from the words of a finite vocabulary, speech recognition by phonemes without vocabulary restriction, recognition of words and phrases by phonemes. Good experimental results were obtained for all algorithmes.