An HMM approach for synthesizing amused speech with a controllable intensity of smile

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.