Scalable Multilingual Frontend for TTS

• URL: http://arxiv.org/abs/2004.04934v1
• Date: Fri, 10 Apr 2020 08:00:40 GMT
• Title: Scalable Multilingual Frontend for TTS
• Authors: Alistair Conkie, Andrew Finch
• Abstract summary: This paper describes progress towards making a Neural Text-to-Speech (TTS) Frontend that works for many languages and can be easily extended to new languages. We take a Machine Translation inspired approach to constructing, and model both text normalization and pronunciation on a sentence level by building and using sequence-to-sequence (S2S) models. For our language-independent approach to pronunciation we do not use a lexicon. Instead all pronunciations, including context-based pronunciations, are captured in the S2S model.
• Score: 4.1203601403593275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes progress towards making a Neural Text-to-Speech (TTS) Frontend that works for many languages and can be easily extended to new languages. We take a Machine Translation (MT) inspired approach to constructing the frontend, and model both text normalization and pronunciation on a sentence level by building and using sequence-to-sequence (S2S) models. We experimented with training normalization and pronunciation as separate S2S models and with training a single S2S model combining both functions. For our language-independent approach to pronunciation we do not use a lexicon. Instead all pronunciations, including context-based pronunciations, are captured in the S2S model. We also present a language-independent chunking and splicing technique that allows us to process arbitrary-length sentences. Models for 18 languages were trained and evaluated. Many of the accuracy measurements are above 99%. We also evaluated the models in the context of end-to-end synthesis against our current production system.

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