This article, based on Khademi's (2021) Master's thesis, examines the use of Google Translate (GT) and its speech capabilities, Text-to-Speech Synthesis (TTS) and Automatic Speech Recognition (ASR), in helping L2 learners acquire the pronunciation of English past -ed allomorphy (/t/, /d/, /id/) in a semi-autonomous context, considering three levels of pronunciation development: phonological awareness, perception, and production. Our pre/posttest results indicate significant improvements in the participants' awareness and perception of the English past -ed, but no improvements in production (except for /id/). These findings corroborate our hypothesis that GT's speech capabilities can be used as pedagogical tools to help learners acquire the target pronunciation feature. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]