Automatic Speech Recognition (ASR) and NMT for Interlingual and Intralingual Communication: Speech to Text Technology for Live Subtitling and Accessibility.

Considered the increasing demand for institutional translation and the multilingualism of international organizations, the application of Artificial Intelligence (AI) technologies in multilingual communications and for the purposes of accessibility has become an important element in the production of translation and interpreting services (Zetzsche, 2019). In particular, the widespread use of Automatic Speech Recognition (ASR) and Neural Machine Translation (NMT) technology represents a recent development in the attempt of satisfying the increasing demand for interinstitutional, multilingual communications at inter-governmental level (Maslias, 2017). Recently, researchers have been calling for a universalistic view of media and conference accessibility (Greco, 2016). The application of ASR, combined with NMT, may allow for the breaking down of communication barriers at European institutional conferences where multilingualism represents a fundamental pillar (Jopek Bosiacka, 2013). In addition to representing a so-called disruptive technology (Accipio Consulting, 2006), ASR technology may facilitate the communication with non-hearing users (Lewis, 2015). Thanks to ASR, it is possible to guarantee content accessibility for non-hearing audience via subtitles at institutionally-held conferences or speeches. Hence the need for analysing and evaluating ASR output: a quantitative approach is adopted to try to make an evaluation of subtitles, with the objective of assessing its accuracy (Romero-Fresco, 2011). A database of F.A.O.’s and other international institutions’ English-language speeches and conferences on climate change is taken into consideration. The statistical approach is based on WER and NER models (Romero-Fresco, 2016) and on an adapted version. The ASR software solution implemented into the study will be VoxSigma by Vocapia Research and Google Speech Recognition engine. After having defined a taxonomic scheme, Native and Non-Native subtitles are compared to gold standard transcriptions. The intralingual and interlingual output generated by NMT is specifically analysed and evaluated via the NTR model to evaluate accuracy and accessibility.