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We compare using a PHOIBLE-based phone mapping method and using phonologicalfeatures input in transfer learning for TTS in low-resource languages. We usediverse source languages (English, Finnish, Hindi, Japanese, and Russian) andtarget languages (Bulgarian, Georgian, Kazakh, Swahili, Urdu, and Uzbek) totest the language-independence of the methods and enhance the findings'applicability. We use Character Error Rates from automatic speech recognitionand predicted Mean Opinion Scores for evaluation. Results show that both phonemapping and features input improve the output quality and the latter performsbetter, but these effects also depend on the specific language combination. Wealso compare the recently-proposed Angular Similarity of Phone Frequencies(ASPF) with a family tree-based distance measure as a criterion to selectsource languages in transfer learning. ASPF proves effective if label-basedphone input is used, while the language distance does not have expectedeffects.

Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection