Abstract
In recent years, machine speech-to-speech and speech-to-text translation has gained momentum thanks to advances in artificial intelligence, especially in the domains of speech recognition and machine translation. The quality of such applications is commonly tested with automatic metrics, such as BLEU, primarily with the goal of assessing improvements of releases or in the context of evaluation campaigns. However, little is known about how such systems compare to human performances in similar communicative tasks or how the performance of such systems is perceived by final users. In this paper, we present the results of an experiment aimed at evaluating the quality of a simultaneous speech translation engine by comparing it to the performance of professional interpreters. To do so, we select a framework developed for the assessment of human interpreters and use it to perform a manual evaluation on both human and machine performances. In our sample, we found better performance for the human interpreters in terms of intelligibility, while the machine performs slightly better in terms of informativeness. The limitations of the study and the possible enhancements of the chosen framework are discussed. Despite its intrinsic limitations, the use of this framework represents a first step towards a user-centric and communication-oriented methodology for evaluating simultaneous speech translation.
Abstract (translated)
URL
https://arxiv.org/abs/2103.08364
