Efficient integration of translation and speech models in dictation based machine aided human translation

This paper is concerned with combining models for decoding an optimum translation for a dictation based machine aided human translation (MAHT) task. Statistical language model (SLM) probabilities in automatic speech recognition (ASR) are updated using statistical machine translation (SMT) model probabilities. The effect of this procedure is evaluated for utterances from human translators dictating translations of source language documents. It is shown that computational complexity is significantly reduced while at the same time word error rate is reduced by 30%.