Speech recognition method and system are adapted to previously prepare a noise pattern in response to environmental noise prior to inputting a speech signal, evaluate a speech feature vector Bi yielded by subtracting the noise pattern from a feature vector Ai of the input speech upon inputting the speech signal thereafter, spectrum-normalize the speech feature vector Bi, evaluate a local peaks vector by making use of binary-coding processing wherein only a component of a channel being the maximum of the spectrum-normalized vector in a direction of frequency is assumed to be '1', evaluate pattern similarity between an input pattern comprising a local peaks vector from the start point to the end point of the input speech and previously prepared reference patterns of a plurality of categories of the same format as the input pattern, and judge a category of a reference pattern being the maximum among the pattern similaritites as a recognized result. As a result, any influence of environmental noise on the speech in concern is eliminated for assuring highly accurate recognition. Furthermore, the speech recognition method and system are adapted to improve efficiency and accuracy of speech recognition by making use of processing of evaluating a new speech feature vector of a prescribed frame number of effecting linear expansion therefor on the basis of internal division of adjoining speech feature vectors Bi and Bi+1, and processing of smoothing a local peaks vector.
Speech recognition for recognizing the catagory of an input speech pattern