We proposed speech-enabled language translation (SELT) technology that consists of speech-to-text recognition (STR) and computer-aided-translation (CAT) systems to support intercultural communication. When the participants spoke in their native languages, the STR system generated texts from their voice input, and the CAT system translated the STR-generated texts in foreign languages into English. We aimed to investigate whether our approach would be useful to facilitate cross-cultural understanding and the intercultural sensitivity of the participants. Our results demonstrated that cross-cultural learning literally took place during the learning activity and that the intercultural sensitivity of the participants improved. Furthermore, low intelligibility and accuracy rates were obtained for Mongolian, Filipino, Hindi, Mandarin, and Vietnamese, while high intelligibility and accuracy rates were obtained for Spanish, Russian and French during the SELT process. Finally, our results showed that the students highly valued the application of SELT as support for the cross-cultural learning activity due to its ease of use, its ability to enable access to authentic content, and the collaboration that occurred among the participants. Based on our results, we provide some implications and useful suggestions to educators and researchers.