Pronunciation by analogy (PbA) is a data-driven approach to phonetic transcription that generates pronunciations for unknown words by exploiting the phonological knowledge implicit in the dictionary that provides the primary source of pronunciations. Unknown words typically include low-frequency ‘common’ words, proper names or neologisms that have not yet been listed in the lexicon. It is received wisdom in the field that knowledge of the class of a word (common versus proper name) is necessary for correct transcription, but in a practical text-to-speech system, we do not know the class of the unknown word
. So if we have a dictionary of common words and another of proper names, we do not know which one to use for analogy unless we attempt to infer the class of unknown words. Such inference is likely to be error prone. Hence it is of interest to know the cost of such errors (if we are using separate dictionaries) and/or the cost of simply using a single, undivided dictionary, effectively ignoring the problem. Here, we investigate the effect of lexicon composition: common words only, proper names only or a mixture. Results suggest that high-transcription accuracy may be achievable without prior classification.
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