  The relationship between written and spoken words is convoluted in languages
with a deep orthography such as English and therefore it is difficult to devise
explicit rules for generating the pronunciations for unseen words.
Pronunciation by analogy (PbA) is a data-driven method of constructing
pronunciations for novel words from concatenated segments of known words and
their pronunciations. PbA performs relatively well with English and outperforms
several other proposed methods. However, the best published word accuracy of
65.5% (for the 20,000 word NETtalk corpus) suggests there is much room for
improvement in it.
  Previous PbA algorithms have used several different scoring strategies such
as the product of the frequencies of the component pronunciations of the
segments, or the number of different segmentations that yield the same
pronunciation, and different combinations of these methods, to evaluate the
candidate pronunciations. In this article, we instead propose to use a
probabilistically justified scoring rule. We show that this principled approach
alone yields better accuracy (66.21% for the NETtalk corpus) than any
previously published PbA algorithm. Furthermore, combined with certain ad hoc
modifications motivated by earlier algorithms, the performance climbs up to
66.6%, and further improvements are possible by combining this method with
other methods.
