Lattice parsing to integrate speech recognition and rule-based machine translation
Selçuk Köprü, Adnan Yazıcı · 2009
In this paper, we present a novel approach to integrate speech recognition and rulebased machine translation by lattice parsing.The presented approach is hybrid in two senses.First, it combines structural and statistical methods for language modeling task.Second, it employs a chart parser which utilizes manually created syntax rules in addition to scores obtained after statistical processing during speech recognition.The employed chart parser is a unification-based active chart parser.It can parse word graphs by using a mixed strategy instead of being bottom-up or top-down only.The results are reported based on word error rate on the NIST HUB-1 word-lattices.The presented approach is implemented and compared with other syntactic language modeling techniques.