Text-to-speech synthesis (TTS) turns a written text into an audio speech signal. Many commercial systems rely on human linguistic expertise, while being limited to synthesize speech for a single speaker voice and speaking style. For speech synthesis to become universal in its usage and abilities, it must be easily customizable while being able to produce widely varied speech. The goal of this thesis is two-fold. 1) To study whether it is possible alleviate the need for human linguistic expertise to build or modify a TTS system. 2) To study whether it is possible to produce speech corresponding to different speakers, with their respective tone and regionalism accent. The thesis works present three contributions. First, we show that the embedding property of neural networks can be used to lower the amount of expertise in unit selection speech synthesis. Second, we show that character embeddings can remove all linguistic expertise for end-to-end systems. Finally, we attempt to explicitly model speaker and accent characteristics in order to build a multi-speaker multi-accent end-to-end speech synthesis system.
Philip N. Garner - Senior researcher – Idiap Research Institute (Suisse)
Pascale Sébillot - Professeure des universités – INSA Rennes / IRISA
Géraldine Damnati - Chercheuse – Orange Labs
Camille Guinaudeau - Maîtresse de conférences – Université Paris-Sud / LIMSI
Laurent Amsaleg - Directeur de recherche – CNRS / IRISA
Gwénolé Lecorvé - Maître de conférences, HDR – Université de Rennes 1 / IRISA
Damien Lolive - Maître de conférences, HDR – Université de Rennes 1 / IRISA