La settimana scorsa, all’ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), Fabio Calefato ha presentato il paper An Empirical Simulation-based Study of Real-Time Speech Translation for Multilingual Global Project Teams., autori: Fabio Calefato, Filippo Lanubile, Rafael Prikladnicki and João Henrique S. Pinto
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.
Il resto è sull’ACM Digital Library.