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An essential design decision for multilingual Neural Text-To-Speech (NTTS)systems is how to represent input linguistic features within the model. Lookingat the wide variety of approaches in the literature, two main paradigms emerge,unified and separate representations. The former uses a shared set of phonetictokens across languages, whereas the latter uses unique phonetic tokens foreach language. In this paper, we conduct a comprehensive study comparingmultilingual NTTS systems models trained with both representations. Our resultsreveal that the unified approach consistently achieves better cross-lingualsynthesis with respect to both naturalness and accent. Separate representationstend to have an order of magnitude more tokens than unified ones, which mayaffect model capacity. For this reason, we carry out an ablation study tounderstand the interaction of the representation type with the size of thetoken embedding. We find that the difference between the two paradigms onlyemerges above a certain threshold embedding size. This study provides strongevidence that unified representations should be the preferred paradigm whenbuilding multilingual NTTS systems.

Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)