Integration of Statistical Models for Dictation of Document Translations in a Machine-Aided Human Translation Task

Abstract--This paper presents a model for machine aided human translation (MAHT) that integrates source language text and target language acoustic information to produce the text translation of source language document. It is evaluated on a scenario where a human translator dictates a first draft target language translation of a source language document. Information obtained from the source language document, including translation probabilities derived from statistical machine translation (SMT) and named entity tags derived from named entity recognition (NER), is incorporated with acoustic phonetic information obtained from an automatic speech recognition (ASR) system. One advantage of the system combination used here is that words that are not included in the ASR vocabulary can be correctly decoded by the combined system. The MAHT model and system implementation is presented. It is shown that a relative decrease in word error rate of 29% can be obtained by this combined system relativ...

• Type: Journal
• Year: 2010
• Where: TASLP
• Authors: Aarthi Reddy, Richard C. Rose
• Keywords: Software Engineering, Source Language Document, Target Language, Target Language Translation, TASLP 2010