The Impact of ASR on Speech-to-Speech Translation Performance

This paper reports on experiments to quantify the impact of automatic speech recognition (ASR) in general and discriminatively trained ASR in particular on the machine translation (MT) performance. The minimum phone error (MPE) training method is employed for building the discriminative ASR acoustic models and a weighted finite state transducer (WEST) based method is used for MT. The experiments are performed on a two-way English/dialectal-Arabic speech-to-speech (S2S) translation task in the military/medical domain. We demonstrate the relationship between ASR and MT performance measured by BLEU and human judgment for both directions of the translation. Moreover, we question the use of BLEU metric for assessing the MT quality, present our observations and draw some conclusions.