The Effects of Input Type and Pronunciation Dictionary Usage in Transfer
  Learning for Low-Resource Text-to-Speech
Phat Do Matt Coler Jelske Dijkstra Esther Klabbers
We compare phone labels and articulatory features as input for cross-lingual
transfer learning in text-to-speech (TTS) for low-resource languages (LRLs).
Experiments with FastSpeech 2 and the LRL West Frisian show that using
articulatory features outperformed using phone labels in both intelligibility
and naturalness. For LRLs without pronunciation dictionaries, we propose two
novel approaches: a) using a massively multilingual model to convert
grapheme-to-phone (G2P) in both training and synthesizing, and b) using a
universal phone recognizer to create a makeshift dictionary. Results show that
the G2P approach performs largely on par with using a ground-truth dictionary
and the phone recognition approach, while performing generally worse, remains a
viable option for LRLs less suitable for the G2P approach. Within each
approach, using articulatory features as input outperforms using phone labels.