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We investigate speech-to-speech translation where one language does not have a well-defined written form. We use English-Spanish and Mandarin-English bitext corpora in order to provide both gold-standard text-based translations and experimental results for different levels of automatically derived symbolic representations from speech. We constrain our experiments such that the methods developed can be extended to low-resource languages. We derive different phonetic representations of the source texts in order to model the kinds of transcriptions that can be learned from low-resource-language speech data. We experiment with different methods of clustering the elements of the phonetic representations together into word-like units. We train MT models on the resulting texts, and report BLEU scores for the different representations and clustering methods in order to compare their effectiveness. Finally, we discuss our findings and suggest avenues for future research.