This paper presents an HMM-based synthesis approach for speechlaughs. The building stone of this project was the idea of the co-occurrence of smile and laughter bursts in varying proportions within amused speech utterances. A corpus with three complementary speaking styles was used to train the underlying HMM models: neutral speech, speech-smile, and finally laughter in different articulatory configurations. Two types of speech-laughs were then synthesized: one made by combining neutral speech and laughter bursts, and the other made by combining speech-smile and laughter bursts. Synthesized stimuli were then rated in terms of perceived amusement and naturalness levels. Results show the compound effect of both laughter bursts and smile on both amusement and naturalness and inspire interesting perspectives.