Speaker trained recognition of large vocabularies of isolated words

It has long been known that one of the key factors in determining the accuracy of isolated word recognition systems is the size and/or complexity of the vocabulary. Although most practical isolated word recognizers use small vocabularies (on the order of 10 to 50 words), there are many applications which require medium to large size vocabularies (e.g. airlines reservation and information, data retrieval etc). It is the purpose of this paper to discuss the problems associated with speaker-trained recognition of a large vocabulary (1109 words) of words. It is shown that the practicability of using large vocabularies for isolated word vocabularies is doubtful, both because of the problems in training the system, and because of the difficulty for the user to learn and remember the vocabulary words for any significant size vocabulary. The importance of studying large word vocabularies for recognition lies in the flexibility it provides for understanding the effects of vocabulary size and complexity on recognition accuracy for both small and medium size vocabularies. By constructing subsets of the total vocabulary for recognition, we show that a judicious choice of words can lead to significantly better recognition accuracy than by poor choice of the words in the subset. We show that for each doubling of the size of the vocabulary, the recognition accuracy tends to decrease by a fixed amount, which is different for each talker.