Google Translate e Microsoft Translator - Valutazione di due applicazioni per la traduzione automatica del parlato e analisi di una tecnologia in evoluzione.

This work focuses on Machine Translation (MT) and Speech-to-Speech Translation, two emerging technologies that allow users to automatically translate written and spoken texts. The first part of this work provides a theoretical framework for the evaluation of Google Translate and Microsoft Translator, which is at the core of this study. Chapter one focuses on Machine Translation, providing a definition of this technology and glimpses of its history. In this chapter we will also learn how MT works, who uses it, for what purpose, what its pros and cons are, and how machine translation quality can be defined and assessed. Chapter two deals with Speech-to-Speech Translation by focusing on its history, characteristics and operation, potential uses and limits deriving from the intrinsic difficulty of translating spoken language. After describing the future prospects for SST, the final part of this chapter focuses on the quality assessment of Speech-to-Speech Translation applications. The last part of this dissertation describes the evaluation test carried out on Google Translate and Microsoft Translator, two mobile translation apps also providing a Speech-to-Speech Translation service. Chapter three illustrates the objectives, the research questions, the participants, the methodology and the elaboration of the questionnaires used to collect data. The collected data and the results of the evaluation of the automatic speech recognition subsystem and the language translation subsystem are presented in chapter four and finally analysed and compared in chapter five, which provides a general description of the performance of the evaluated apps and possible explanations for each set of results. In the final part of this work suggestions are made for future research and reflections on the usability and usefulness of the evaluated translation apps are provided.