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.