Previous work on cross-lingual transfer learning in text-tospeech has shown the effectiveness of fine-tuning phonemic representations on small amounts of target language data. In other contexts, phonological features (PFs) have been suggested as a more suitable input representation than phonemes for sharing acoustic information between languages, for example in multilingual model training or for code-switching synthesis where an utterance may contain words from multiple languages. Starting from a model trained on 14 hours of English, we find that cross-lingual fine-tuning with 15 minutes of German data can produce speech with subjective naturalness ratings comparable to a model trained from scratch on 4 hours of German, using either phonemes or PFs. We also find a modest but statistically significant improvement in naturalness ratings using PFs over phonemes when training from scratch on 4 hours of German.