Continuous speech recognition via diphone spotting a preliminary implementation

The paper describes a preliminary implementation of a continuous speech recognition system based on the concept of diphone spotting. This consists of continuously measuring the similarity of the current portion of signal pattern with a complete set of selected phonetic events, called diphones because the most significant of them are transitions between pairs of phonemes. The entire set of measures feeds a linguistic decoder that operates on a state space representation of the language, whose states are the diphones that compose the words of the lexicon. Durational constraints and optional phonological rules are included in the language representation. The linguistic decoder recognizes the sentence as that path through the network which attains the highest cumulative similarity score. Additionally, the decoder has the task of detecting the end of the sentence. A preliminary test on 50 sequences of 3 to 7 connected digits gave recognition rates as high as 99.6% on digits, or 98% on sequences, thus confirming the validity of this approach.