Multilingual Modelling And Adaptation For Text-To-Speech Synthesis In Low-Resource Languages

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics

Abstract

Previous work on cross-lingual transfer learning in text-to- speech has shown the effectiveness of fine-tuning phonemic representations on small amounts of target language data. In other contexts, phonological features (PFs) have been suggested as a more suitable input representation than phonemes for sharing acoustic information between languages, for example in multilingual model training or for code-switching synthesis where an utterance may contain words from multiple languages. Starting from a model trained on 14 hours of English, we find that cross-lingual fine-tuning with 15 minutes of German data can produce speech with subjective naturalness ratings comparable to a model trained from scratch on 4 hours of German, using either phonemes or PFs. We also find a modest but statistically significant improvement in naturalness ratings using PFs over phonemes when training from scratch on 4 hours of German.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022481/1 31/03/2019 29/09/2027
2260597 Studentship EP/S022481/1 31/08/2019 30/08/2023 Dan Wells