Recognition of monosyllabic words in continuous sentences using composite word templates

As a preliminary step in syllable recognition in continuous speech, differences in acoustic forms were studied when these are induced by variation in the syntactic role of the word in the sentences. A modified dynamic programming algorithm is presented that allows building up of reference information from a speaker's productions in the face of such variations. The limited data base studied included 169 productions occurring in 57 sentences and composed of 52 different monosyllabic CVC words spoken by two male speakers. When each speaker's productions were tested against reference data built up from different tokens of his productions, correct first choice recognition was attained for 83% and 90% of the words, respectively. A similarity metric that takes into account the detailed acoustic variation that each speech sound may undergo without loss of identifiability can be expected to improve on these results.