Speech Translation by Confusion Network Decoding

Abstract

This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it presents an implementation of a confusion network decoder which significantly improves both in efficiency and performance previous work along this direction. The confusion network decoder results as an extension of a state-of-the-art phrase-based text translation system. Experimental results in terms of decoding speed and translation accuracy are reported on a real-data task, namely the translation of plenary speeches at the European Parliament from Spanish to English.