  Despite the growing interest for expressive speech synthesis, synthesis of
nonverbal expressions is an under-explored area. In this paper we propose an
audio laughter synthesis system based on a sequence-to-sequence TTS synthesis
system. We leverage transfer learning by training a deep learning model to
learn to generate both speech and laughs from annotations. We evaluate our
model with a listening test, comparing its performance to an HMM-based laughter
synthesis one and assess that it reaches higher perceived naturalness. Our
solution is a first step towards a TTS system that would be able to synthesize
speech with a control on amusement level with laughter integration.
