Use of a priori knowledge of vocabulary for real time discrete utterance recognition

Performance degradation of isolated word recognition systems on vocabularies with acoustically similar words is a well known problem. In many cases, vocabularies can be designed to eliminate this confusability. However, the English alphabet combined with digits is a fixed vocabulary which cannot be easily modified to be less confusable. We show how a priori phonetic knowledge of the alpha-digits vocabulary can be used during template generation to reduce error rate. Unlike methods proposed by Lamel and Zue or Rabiner and Wilpon, our technique requires only a single pass for recognition. Thus it is usable in a real time system. The alpha-digits vocabulary is partitioned into subclasses of words with common vowel phonemes, or in some cases allophones. Each subclass consists of words which differ only in the starting or final consonants. Templates are created so the common vowel in each subclass has one representation. For words in the same subclass, the match from the dynamic programming score reflects only initial or final consonant differences. Template storage space is reduced because the vowel portion of each template in the subclass is stored only once.