Syntactic decision rules for recognition of spoken words and phrases using a stochastic automaton

This study deals with the design of a syntactic decision rule for recognizing an unknown utterance from a set X. The decision rule is expressed as a function of the character string (CS) derived from the test utterance. To obtain the CS, the waveform of the utterance is divided into a large number of frames of roughly equal duration numbered 1, 2,...,n. The ith symbol in the CS is the phonemic symbol obtained by subjecting the ith frame of the waveform to a relatively simple phoneme decision rule, the number of symbols in the CS being n. All the available nonacoustic information such as the lexicon of words in the set X, the possibility of confusion between different phonemes as seen by the phoneme decision rule, etc. is used in the design of the decision rule. The syntactic decision rule can be implemented by a stochastic finite state automaton involving limited memory and computation. The decision rule can also be interpreted as yielding the phrase x which minimizes a distance measure D(x, z) between the phrase x X and the observed CS z. We wili compare this approach with the other approaches such as the Viterbi methods, the distance approaches involving various types of distances, etc.