  Code-switching---the intra-utterance use of multiple languages---is prevalent
across the world. Within text-to-speech (TTS), multilingual models have been
found to enable code-switching. By modifying the linguistic input to
sequence-to-sequence TTS, we show that code-switching is possible for languages
unseen during training, even within monolingual models. We use a small set of
phonological features derived from the International Phonetic Alphabet (IPA),
such as vowel height and frontness, consonant place and manner. This allows the
model topology to stay unchanged for different languages, and enables new,
previously unseen feature combinations to be interpreted by the model. We show
that this allows us to generate intelligible, code-switched speech in a new
language at test time, including the approximation of sounds never seen in
training.
