Applications of information theory to pattern recognition and the design of decision trees and trellises
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Phonetic baseforms are the basic recognition units in most large vocabulary speech recognition systems. These base-forms are usually determined by hand once a vocabulary is chosen and not modified thereafter. However, many applications of speech recognition, such as dictation transcription, are hampered by a fixed vocabulary and require the user be able to add new words to the vocabulary. At least one phonetic baseform must be assigned to each new word to properly integrate the word into the recognition system. Dictionary lookup is often unsuccessful in determining a phonetic baseform because new words are often names or task-specific jargon; also, talkers tend to have idiosyncratic pronunciations for a substantial fraction of words. This paper describes a series of experiments in which the phonetic baseform is deduced automatically for new words by utilizing actual utterances of the new word in conjunction with a set of automatically derived spelling-to-sound rules. We evaluated recognition performance on new words spoken by two different talkers when the phonetic baseforms were extracted via the above approach. The error rates on these new words were found to be comparable to or better than when the phonetic baseforms were derived by hand, thus validating the basic approach.