Lattice parsing to integrate speech recognition and rule-based machine translation

Abstract

In this paper, we present a novel approach to integrate speech recognition and rule-based 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. © 2009 Association for Computational Linguistics.