This Master’s thesis focuses on the unexplored domain of animal vocalizations. In the present work Lemur (madagascarian mammal) vocalizations are studied and analysed in order to create a system that can process and train datasets and posteriorly synthesize vocalizations. To achieve the goal the technique of Hidden Markov Models based synthesis is used with the HMM-based speech synthesis system (HTS) toolbox, based on the wide known Hidden Markov Model Toolkit (HTK) speech recognition system. In the following pages a system to synthesize lemur vocalizations is presented and its advantages and inconveniences defined.