Continuous speech recognition using syllables

The vast majority of work in continuous speech recognition uses phoneme-like units as the basic recognition component. The work presented here investigates the practicability of syllable-like units as the building blocks for recognition. A phonetically annotated telephony database is analysed at the syllable level, and a set of syllable-based HMMs are built. Refinements including the introduction of syllable-level bigram probabilities, wordand syllable-level insertion penalties, and the investigation of different model topologies are found to improve recogniser performance. It is found that the syllablebased recogniser gives recognition accuracies of over 60%, which compares with 35% as the baseline accuracy for monophone recognition. It is envisaged that practical applications of syllable recognition could be in a hybrid system, where the most common syllable HMMs would be used in conjunction with whole-word and phoneme models.