Multilingual Byte2Speech Text-To-Speech Models Are Few-shot Spoken Language Learners

• URL: http://arxiv.org/abs/2103.03541v1
• Date: Fri, 5 Mar 2021 08:41:45 GMT
• Title: Multilingual Byte2Speech Text-To-Speech Models Are Few-shot Spoken Language Learners
• Authors: Mutian He, Jingzhou Yang, Lei He
• Abstract summary: We present a multilingual end-to-end Text-To-Speech framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts. The framework demonstrates capabilities to adapt to various new languages under extreme low-resource scenarios. We propose a novel method to extract language-specific sub-networks for a better understanding of the mechanism of multilingual models.
• Score: 11.190877290770047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a multilingual end-to-end Text-To-Speech framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts. Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording without the need of lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models. Exhaustive comparative studies are performed to reveal the potential of the framework for low-resource application and the impact of various factors contributory to adaptation. Furthermore, we propose a novel method to extract language-specific sub-networks for a better understanding of the mechanism of multilingual models.

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