Automatic, data-driven grapheme-to-phoneme conversion is a challenging but often necessary task. The top-down strategy implicitly followed by traditional inductive learning techniques tends to dismiss relevant contexts when they have been seen too infrequently in the training data. The bottom-up philosophy inherent in, e.g., pronunciation by analogy, allows for a markedly better handling of rarer contexts, but proves nonetheless equally dependent on local, language-dependent alignments between letters and phonemes. This paper proposes an alternative bottom-up approach, dubbed pronunciation by latent analogy, which adopts a global definition of analogy, more amenable to obviate such supervision. For each out-of-vocabulary word, a neighborhood of globally relevant pronunciations is constructed through an appropriate data-driven mapping of its graphemic form. Phoneme transcription then proceeds via locally optimal sequence alignment and maximum likelihood position scoring. This method was successfully applied to the speech synthesis of proper names with a large diversity of origin.
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