Assessing speech-to-speech translation quality: Case study of the ILA S2S app

Abstract

Machine translation (MT) is becoming qualitatively more successful and quantitatively more productive at an unprecedented pace. It is becoming a widespread solution to the challenges of a constantly rising demand for quick and affordable translations of both text and speech, causing disruption and adjustments of the translation practice and profession, but at the same time making multilingual communication easier than ever before. This paper focuses on the speech-to-speech (S2S) translation app Instant Language Assistant (ILA), which brings together the state-of-the-art translation technology: automatic speech recognition, machine translation and text-to-speech synthesis, and allows for MT-mediated multilingual communication. The aim of the paper is to assess the quality of translations of conversational language produced by the S2S translation app ILA for en-de and en-hr language pairs. The research includes several levels of translation quality analysis: human translation quality assessment by translation experts using the Fluency/Adequacy Metrics, light-post editing, and automated MT evaluation (BLEU). Moreover, the translation output is assessed with respect to language pairs to get an insight into whether they affect the MT output quality and how. The results show a relatively high quality of translations produced by the S2S translation app ILA across all assessment models and a correlation between human and automated assessment results.