Recent advances on speech technologies (automatic speech recognition, ASR, and text-to-speech, TTS, synthesis) have led to their integration in computer-assisted pronunciation training (CAPT) tools. However, pronunciation is an area of teaching that has not been developed enough since there is scarce empirical evidence assessing the effectiveness of CAPT tools and games that include ASR/TTS. In this manuscript, we summarize the findings presented in Cristian Tejedor-García's Ph.D. Thesis (University of Valladolid, 2020). In particular, this dissertation addresses the design and validation of an innovative CAPT system for smart devices for training second language (L2) pronunciation at the segmental level with a specific set of methodological choices, such as the inclusion of ASR/TTS technologies with minimal pairs, learner's native–foreign language connection, a training cycle of exposure–perception– production, and individual/social approaches. The experimental research conducted applying these methodological choices with real users validates the efficiency of the CAPT prototypes developed for the four main experiments of this dissertation about English and Spanish as L2. We were able to accurately measure the relative pronunciation improvement of the individuals who trained with them. Expert raters on phonetics' subjective scores and CAPT's objective scores showed a strong correlation, being useful in the future to be able to assess a large amount of data and reducing human costs.