Suprasegmentals in very large vocabulary isolated word recognition

Prosodic information is believed to be valuable informnation in human speech perception, but speech recognition systems to date have largely been based on segmental spectral analysis. In this paper I describe parts of a front end to a very-large-vocabulary isolated word recognition system using prosodic information. The present front end is template independent (speaker training for large vocabulary systems (> 20,000 words) is undesirable) and makes use of robust cues in the incoming speech to obtain a presorted vocabulary of candidates. It is shown that prosodic information, e.g., the rhythmic structure of an input word, its syllabic structure, voiced/unvoiced regions in the word and the temporal distribution of back/front vowels, nasals and liquids and glides, can be used effectively to select a substantially reduced subvocabulary of candidates, before any fine phonetic analysis is attempted to recognize the word.