Towards domain independence in machine aided human translation

This paper presents an approach for integrating statistical machine translation and automatic speech recognition for machine aided human translation (MAHT). It is applied to the problem of improving ASR performance for a human translator dictating translations in a target language while reading from a source language document. The approach addresses the issues associated with task independent ASR including out of vocabulary words and mismatched language models. We show in this paper that by obtaining domain information from the document in the form of labelled named entities from the source language text the accuracy of the ASR system can be improved by 34.5%.