Algorithms for statistical translation of spoken language

Abstract

We describe three approaches to statistical translation and present experimental results. The statistical translation approach uses two types of information: a translation model and a language model. The language model used is a bigram or general m-gram model. The translation model is decomposed into a lexical model and an alignment model. There are three approaches that are presented and tested in detail: the quasi-monotone alignment approach, the inverted alignment approach, and the alignment template approach. For each of these three approaches, a suitable search method is presented. The system has been tested on a limited-domain spoken-language task for which a bilingual corpus is available: the Verbmobil task (German-English, 7000-word vocabulary). We present experimental results for each of the three approaches. The experimental tests were performed on both the text transcription and the speech recognizer output.