Date of Original Version
Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL)
Copyright 2014 Association for Computational Linguistics
Abstract or Description
We propose a novel technique for adapting text-based statistical machine translation to deal with input from automatic speech recognition in spoken language translation tasks. We simulate likely misrecognition errors using only a source language pronunciation dictionary and language model (i.e., without an acoustic model), and use these to augment the phrase table of a standard MT system. The augmented system can thus recover from recognition errors during decoding using synthesized phrases. Using the outputs of five different English ASR systems as input, we find consistent and significant improvements in translation quality. Our proposed technique can also be used in conjunction with lattices as ASR output, leading to further improvements.
Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL), 616-625.