This bachelor’s thesis explores different ways of building a block-based Speech Translation system with the aim of generating huge amounts of parallel speech data. The first goal is to research and manage to run suitable tools to implement each one of the three blocks that integrates the Speech Translation system: Speech Recognition, Translation and Speech Synthesis. We experiment with some open-source toolkits and we manage to train a speech recognition system and a neural machine translation system. Then, we test them to evaluate their performance. As an alternative option, we use the cloud computing solutions provided by Google Cloud to implement the three sequential blocks and we successfully build the overall system. Finally, we make a comparative study between an in-house software development versus Cloud computing implementation.