We introduce phylogenetic and areal language features to the domain of multilingual
text-to-speech (TTS) synthesis. Intuitively, enriching the existing universal
phonetic features with such cross-language shared representations should benefit
the multilingual acoustic models and help to address issues like data scarcity for
low-resource languages. We investigate these representations using the acoustic
models based on long short-term memory (LSTM) recurrent neural networks (RNN).
Subjective evaluations conducted on eight languages from diverse language families
show that sometimes phylogenetic and areal representations lead to significant
multilingual synthesis quality improvements.