In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose. It contains data for male and female actors in English and a male actor in French. The database covers 5 emotion classes so it could be suitable to build synthesis and voice transformation systems with the potential to control the emotional dimension in a continuous way. We show the data's efficiency by building a simple MLP system converting neutral to angry speech style and evaluate it via a CMOS perception test. Even though the system is a very simple one, the test show the efficiency of the data which is promising for future work.
 Smile is not only a visual expression. When it occurs together with speech, it also alters its acoustic realization. Being able to synthesize speech altered by the expression of smile can hence be an important contributor for adding naturalness and expressiveness in interactive systems. In this work, we present a first attempt to develop a Hidden Markov Model (HMM)-based synthesis system allowing to control the degree of smile in speech. It relies on a model interpolation technique, enabling speech-smile sentences with various smiling intensities to be generated. Sentences synthesized using this approach have been evaluated through a perceptual test. Encouraging results are reported here. In this paper we present the AmuS database of about three hours worth of data related to amused speech recorded from two males and one female subjects and contains data in two languages French and English. We review previous work on smiled speech and speech-laughs. We describe acoustic analysis on part of our database, and a perception test comparing speech-laughs with smiled and neutral speech. We show the efficiency of the data in AmuS for synthesis of amused speech by training HMM-based models for neutral and smiled speech for each voice and comparing them using an on-line CMOS test. In this work, we present a study dedicated to improve the speech-laugh synthesis quality. The impact of two factors is evaluated. The first factor is the addition of breath intake sounds after laughter bursts in speech. The second is the repetition of the word interrupted by laughs in the speech-laugh sentences. Several configurations are evaluated through subjective perceptual tests. We report an improvement of the synthesized speech-laugh naturalness when the breath intake sounds are added. We were unable, though, to make a conclusion concerning a possible positive impact of the repetition of the interrupted words on the speech-laugh synthesis quality.