Study of Unit Selection Text-To-Speech Synthesis Algorithms

This book focuses on the automatic speech synthesis field, and more specifically on unit selection. A deep analysis and a diagnosis of the unit selection algorithm (a lattice search algorithm) is provided. The importance of having the optimal solution is discussed and a new unit selection implementation based on a A algorithm is presented. The IRISA TTS system, built for the study, is also presented. Three cost function enhancements are also presented. The first one is a new way - in the target cost - to minimize important spectral differences by selecting sequences of candidate units that minimize a mean cost instead of an absolute one. This cost is tested on a phonemic duration distance but is applicable to others. Our second proposition is a target sub-cost addressing intonation. It is based on coefficients extracted through a generalized version of Fujisaki's command-response model. This model features gamma functions modeling F0 called atoms. Finally, our third contribution concerns a penalty system that aims at enhancing the concatenation cost. This system is tempered by a fuzzy function that allows to soften penalties for units presenting low concatenation costs.

Autorentext

David Guennec obtained his PhD in computer science at the university of Rennes 1 in 2016. His work focuses on automatic speech synthesis. He is one of the creators of the IRISA TTS System.