Acquiring data for text-to-speech (TTS) systems is expensive. This typically requires large amounts of training data, which is not available for low-resourced languages. Sometimes small amounts of data can be collected, while often - no data may be available at all. This paper presents acoustic modeling approach utilizing long short-term memory (LSTM) recurrent neural network (RNN) aimed at partially addressing the language data scarcity problem. Unlike speaker-adaption systems that aim to preserve speaker similarity across languages, the salient feature of the proposed approach is that, once constructed, the resulting system does not need retraining to cope with the previously unseen languages. This is due to language and speaker-agnostic model topology and universal linguistic feature set. Experiments on twelve languages show that the system is able to produce intelligible and sometimes natural output when language is unseen. We also show that, when small amounts of training data are available, pooling the data sometimes improves the overall intelligibility and naturalness. Finally, we show that sometimes having a multilingual system with no prior exposure to the language is better than building single-speaker system from small amounts of data for that language.