  The idea of using phonological features instead of phonemes as input to
sequence-to-sequence TTS has been recently proposed for zero-shot multilingual
speech synthesis. This approach is useful for code-switching, as it facilitates
the seamless uttering of foreign text embedded in a stream of native text. In
our work, we train a language-agnostic multispeaker model conditioned on a set
of phonologically derived features common across different languages, with the
goal of achieving cross-lingual speaker adaptation. We first experiment with
the effect of language phonological similarity on cross-lingual TTS of several
source-target language combinations. Subsequently, we fine-tune the model with
very limited data of a new speaker's voice in either a seen or an unseen
language, and achieve synthetic speech of equal quality, while preserving the
target speaker's identity. With as few as 32 and 8 utterances of target speaker
data, we obtain high speaker similarity scores and naturalness comparable to
the corresponding literature. In the extreme case of only 2 available
adaptation utterances, we find that our model behaves as a few-shot learner, as
the performance is similar in both the seen and unseen adaptation language
scenarios.
