Multilingual context-based pronunciation learning for Text-to-Speech

• URL: http://arxiv.org/abs/2307.16709v1
• Date: Mon, 31 Jul 2023 14:29:06 GMT
• Title: Multilingual context-based pronunciation learning for Text-to-Speech
• Authors: Giulia Comini, Manuel Sam Ribeiro, Fan Yang, Heereen Shim, Jaime Lorenzo-Trueba
• Abstract summary: Phonetic information and linguistic knowledge are an essential component of a Text-to-speech (TTS) front-end. We showcase a multilingual unified front-end system that addresses any pronunciation related task, typically handled by separate modules. We find that the multilingual model is competitive across languages and tasks, however, some trade-offs exists when compared to equivalent monolingual solutions.
• Score: 13.941800219395757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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