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We consider the problem of integrating supplemental information strings in the grapheme-to-phoneme (G2P) conversion task. In particular, we investigate whether we can improve the performance of a G2P system by making it aware of corresponding transductions of an external knowledge source, such as transcriptions in other dialects or languages, transcriptions provided by other datasets, or transcriptions obtained from crowd-sourced knowledge bases such as Wiktionary. Our main methodological paradigm is that of multiple monotone many-to-many alignments of input strings, supplemental information strings, and desired transcriptions. Subsequently, we apply a discriminative sequential transducer to the multiply aligned data, using subsequences of the supplemental information strings as additional features.