Text normalization is the task of converting from a written representation into a representation of how the text is to be spoken. For most real-world speech applications, the text normalization engine is developed mostly by hand. For example, a hand-built grammar may be used to enumerate possible ways to say a given token in a given language, and a statistical model used to select the most appropriate verbalizations in context. We examine the tradeoffs associated with using more or less language-specific knowledge for text normalization. In the most data-rich scenario, we have access to a carefully constructed hand-built normalization grammar that for any given token will produce a lattice of all possible verbalizations for that token. We assume a parallel corpus of aligned written-spoken utterances. As a substitute for the hand-built grammar, we consider a language-universal normalization covering grammar, where the developer merely needs to provide a set of lexical items particular to the language. As a substitute for the aligned corpus, we consider a scenario where one only has the spoken side, and the corresponding written side is “hallucinated” by composing the spoken side with the inverted normalization grammar. We report performance of the above scenarios on experiments with English and Russian.