Multilingual context-based pronunciation learning for Text-to-Speech
Giulia Comini Manuel Sam Ribeiro Fan Yang Heereen Shim Jaime Lorenzo-Trueba
Phonetic information and linguistic knowledge are an essential component of a
Text-to-speech (TTS) front-end. Given a language, a lexicon can be collected
offline and Grapheme-to-Phoneme (G2P) relationships are usually modeled in
order to predict the pronunciation for out-of-vocabulary (OOV) words.
Additionally, post-lexical phonology, often defined in the form of rule-based
systems, is used to correct pronunciation within or between words. In this work
we showcase a multilingual unified front-end system that addresses any
pronunciation related task, typically handled by separate modules. We evaluate
the proposed model on G2P conversion and other language-specific challenges,
such as homograph and polyphones disambiguation, post-lexical rules and
implicit diacritization. We find that the multilingual model is competitive
across languages and tasks, however, some trade-offs exists when compared to
equivalent monolingual solutions.