2006. Integration of speech to computer-assisted translation using finite-state automata

State-of-the-art computer-assisted transla-tion engines are based on a statistical pre-diction engine, which interactively pro-vides completions to what a human trans-lator types. The integration of human speech into a computer-assisted system is also a challenging area and is the aim of this paper. So far, only a few methods for integrating statistical machine transla-tion (MT) models with automatic speech recognition (ASR) models have been stud-ied. They were mainly based on N-best rescoring approach. N-best rescor-ing is not an appropriate search method for building a real-time prediction engine. In this paper, we study the incorporation of MT models and ASR models using finite-state automata. We also propose some transducers based on MT models for rescoring the ASR word graphs.