Applying Speech-to-Text Recognition and Computer-Aided Translation for Supporting Multi-lingual Communications in Cross-Cultural Learning Project

Abstract

We applied a speech-to-text recognition (STR) and computer-aided translation (CAT) systems to support multi-lingual communications students participating in cross-cultural learning project. The participants were engaged in interactions and information exchanges in order to learn and understand cultures and traditions of their peers. Their communications were carried out in their native languages on social communication platforms. The participants spoke and STR system generated texts from their voice inputs. CAT system then simultaneously translated STR-texts into English. Finally, translated texts were posted on social communication platforms along with spoken content in the participants' native languages. We aimed to examine accuracy rates of processes associated with STR and CAT for different languages during multi-lingual communications in our cross-cultural learning project. In addition, the feasibility of our approach to support multi-lingual communications in cross-cultural learning project was investigated. Our results showed that the lowest accuracy rate was for Mongolian and Filipino and the highest was for Spanish, Russian, and French. Our results also demonstrated that cross-cultural learning took place, the participants understood and were able to explain foreign traditions to others as well as to compare foreign traditions with their own local. Based on our results, we made several suggestions and implications for the teaching and research community.