Barra-Chicote, Roberto

King, Simon

Lorenzo-Trueba, Jaime

Montero, Juan M

Watts, Oliver

Yamagishi, Junichi

English

Simple4All is a European funded project that aims to streamline the production of multilanguage expressive synthetic voices by means of unsupervised data extraction techniques, allowing the automatic processing of freely available data into flexible task-specific voices. In this paper we describe three different approaches for this task, the first two covering enhancements in expressivity and flexibility with the final one focusing on the development of unsupervised voices. The first technique introduces the principle of speaker adaptation from average models consisting of multiple voices, with the second being an extension of this adaptation concept into allowing the control of the expressive strength of the synthetic voice. Finally, an unsupervised approach to synthesis capable of learning from unlabelled text data is introduced in detai

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