Laughter is a key signal in human communication, conveying information about our emotional state but also providing social feedback to the conversational partners. With the development of more and more natural human-computer interactions (with the help of embodied conversational agents, etc.), the need emerged to enable computers to understand and express emotions. In particular, to enhance human-computer interactions, talking machines should be able to laugh. To improve laughter synthesis naturalness, we propose to use Hidden Markov Models (HMMs), which have proven efficient for speech synthesis.