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In this project, we aim to build a Text-to-Speech system able to producespeech with a controllable emotional expressiveness. We propose a methodologyfor solving this problem in three main steps. The first is the collection ofemotional speech data. We discuss the various formats of existing datasets andtheir usability in speech generation. The second step is the development of asystem to automatically annotate data with emotion/expressiveness features. Wecompare several techniques using transfer learning to extract such arepresentation through other tasks and propose a method to visualize andinterpret the correlation between vocal and emotional features. The third stepis the development of a deep learning-based system taking text andemotion/expressiveness as input and producing speech as output. We study theimpact of fine tuning from a neutral TTS towards an emotional TTS in terms ofintelligibility and perception of the emotion.

A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approach