Method and system for identifying and recognizing speech

Abstract

Improved system and method for speaker-independent speech token recognition are described. The system is neural network-based and involves processing a sequence of spoken utterances, e.g. separately articulated letters of a name, to identify the same based upon a highest probability match of each utterance with learned speech tokens, e.g. the letters of the English language alphabet, and based upon a highest probability match of the uttered sequence with a defined utterance library, e.g. a list of names. First, the spoken utterance is digitized or captured and processed into a spectral representation. Second, discrete time frames of the DFT are classified phonetically. Third, the time-frame outputs are used by a modified Viterbi search to locate segment boundaries, near which such segment boundaries lies the information that is needed to discriminate letters. Fourth, the segmented or bounded representation is reclassified using such information into individual hypothesized letters. Fifth, successive, hypothesized letter scores are analyzed to obtain a high probability match with a spelled word within the utterance library. The system and method comprehend finer distinctions near points of interest used to discriminate difficult-to-recognize letter pair differences such as M/N, B/D, etc. The system is described in the context of phone line reception of names spelled by remote users.