Coupling speech recognition and rule-based machine translation

Thesis Type: Doctorate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Computer Engineering, Turkey

Approval Date: 2008


Consultant: ADNAN YAZICI


The objective of this thesis was to study the coupling of automatic speech recognition (ASR) systems with rule-based machine translation (MT) systems. In this thesis, a unique approach to integrating ASR with MT for speech translation (ST) tasks was proposed. The proposed approach is unique, essentially because it includes the rst rule-based MT system that can process speech data in a word graph format. Compared to other rule-based MT systems, our system processes both a word graph and a stream of words. Thus, the suggested integration method of the ASR and the rule-based MT system is more detailed than a simple software engineering practice. The second reason why it is unique is because this coupling approach performed better than the rst-best and N-best list techniques, which are the only other methods used to integrate an ASR with a rule-based MT system. The enhanced performance of the coupling approach was verified with experiments. The utilization of rule-based MT systems for ST tasks is important; however, there are some unresolved issues. Most of the literature concerning coupling systems has focused on how to integrate ASR with statistical MT rather than rule-based MT. This is because statistical MT systems can process word graphs as input, and therefore, the resolution of ambiguities can be moved to the MT component. With the new approach proposed in this thesis, this same advantage exists in rule-based MT systems. The success of such an approach could facilitate the efficient usage of rule-based systems for ST tasks.