An Empirical Simulation-based Study of Real-Time Speech Translation for Multilingual Global Project Teams

ABSTRACT Context: Real-time speech translation technology is today available but still lacks a complete understanding of how such technology may affect communication in global software projects. Goal: To investigate the adoption of combining speech recognition and machine translation in order to overcome language barriers among stakeholders who are remotely negotiating software requirements. Method: We performed an empirical simulation-based study including: Google Web Speech API and Google Translate service, two groups of four subjects, speaking Italian and Brazilian Portuguese, and a test set of 60 technical and non-technical utterances. Results: Our findings revealed that, overall: (i) a satisfactory accuracy in terms of speech recognition was achieved, although significantly affected by speaker and utterance differences; (ii) adequate translations tend to follow accurate transcripts, meaning that speech recognition is the most critical part for speech translation technology. Conclusions: Results provide a positive albeit initial evidence towards the possibility to use speech translation technologies to help globally distributed team members to communicate in their native languages

Towards Development of a Multilingual Mobile Chat Application for Enhanced Global Communication

The advent of mobile chat applications has revolutionized everyday communication. These applications facilitate the exchange of user's textual and multimedia content across languages and cultures. Most chat applications are known to only support a limited set of predominantly spoken languages, thereby, leaving a substantial portion of the user population without adequate multilingual support. This paper aims to bridge the linguistic gap by presenting Kobapp, a multilingual chat application. The Kobapp, leverages some of the cutting-edge technologies, such as React-Native, Next.js, and the DeepL API, to offer real-time, accurate translations while at the same time offering user privacy. The development process of the Kobapp is outlined from the system architecture and design, emphasizing the integration of a client-side (Android) and server-side using Node.js, Express.js, and MongoDB. Notably, user feedback plays a crucial role in shaping an application's features and functionality. Therefore, the application’s performance was evaluated through a conducted user study. Results of the study indicate a strong positive linear relationship between overall user satisfaction and translation accuracy for different language pairs. Moreover, the absence of outliers and the model's significance further reinforces the application's commitment to data quality and accuracy. Future research will explore new dimensions in multilingual communication and applications to promote a truly global community

An Empirical Simulation-based Study of Real-Time Speech Translation for Multilingual Global Project Teams

Context: Real-time speech translation technology is today available but still lacks a complete understanding of how such technology may affect communication in global software projects. Goal: To investigate the adoption of combining speech recognition and machine translation in order to overcome language barriers among stakeholders who are remotely negotiating software requirements. Method: We performed an empirical simulation-based study including: Google Web Speech API and Google Translate service, two groups of four subjects, speaking Italian and Brazilian Portuguese, and a test set of 60 technical and non-technical utterances. Results: Our findings revealed that, overall: (i) a satisfactory accuracy in terms of speech recognition was achieved, although significantly affected by speaker and utterance differences; (ii) adequate translations tend to follow accurate transcripts, meaning that speech recognition is the most critical part for speech translation technology. Conclusions: Results provide a positive albeit initial evidence towards the possibility to use speech translation technologies to help globally distributed team members to communicate in their native languages