Multilingual Text-to-Speech Synthesis for Turkic Languages Using
  Transliteration
Rustem Yeshpanov Saida Mussakhojayeva Yerbolat Khassanov
This work aims to build a multilingual text-to-speech (TTS) synthesis system
for ten lower-resourced Turkic languages: Azerbaijani, Bashkir, Kazakh, Kyrgyz,
Sakha, Tatar, Turkish, Turkmen, Uyghur, and Uzbek. We specifically target the
zero-shot learning scenario, where a TTS model trained using the data of one
language is applied to synthesise speech for other, unseen languages. An
end-to-end TTS system based on the Tacotron 2 architecture was trained using
only the available data of the Kazakh language. To generate speech for the
other Turkic languages, we first mapped the letters of the Turkic alphabets
onto the symbols of the International Phonetic Alphabet (IPA), which were then
converted to the Kazakh alphabet letters. To demonstrate the feasibility of the
proposed approach, we evaluated the multilingual Turkic TTS model subjectively
and obtained promising results. To enable replication of the experiments, we
make our code and dataset publicly available in our GitHub repository.