  We present in this paper an original extension of two data-driven algorithms
for the transcription of a sequence of graphemes into the corresponding
sequence of phonemes. In particular, our approach generalizes the algorithm
originally proposed by Dedina and Nusbaum (D&N) (1991), which had originally
been promoted as a model of the human ability to pronounce unknown words by
analogy to familiar lexical items. We will show that DN's algorithm performs
comparatively poorly when evaluated on a realistic test set, and that our
extension allows us to improve substantially the performance of the
analogy-based model. We will also suggest that both algorithms can be
reformulated in a much more general framework, which allows us to anticipate
other useful extensions. However, considering the inability to define in these
models important notions like lexical neighborhood, we conclude that both
approaches fail to offer a proper model of the analogical processes involved in
reading aloud.
