Learning pronunciation from a foreign language in speech synthesis networks

Younggun Lee, Taesu Kim

Oct 22, 2018 (modified: Nov 16, 2018) NIPS 2018 Workshop IRASL Blind Submission readers: everyone
  • Abstract: Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronunciation often reflect their native language's characteristics. That motivates us to investigate how the speech synthesis network learns the pronunciation when multi-lingual dataset is given. In this study, we train the speech synthesis network bilingually in English and Korean, and analyze how the network learns the relations of phoneme pronunciation between the languages. Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages. Based on the result, we also show that it is possible to train networks that synthesize English speaker's Korean speech and vice versa. In another experiment, we train the network with limited amount of English dataset and large Korean dataset, and analyze the required amount of dataset to train a resource-poor language with the help of resource-rich languages.
  • TL;DR: Learned phoneme embeddings of multilingual neural speech synthesis network could represent relations of phoneme pronunciation between the languages.
  • Keywords: multilingual data, phoneme representation, text-to-speech, speech synthesis
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