Efficient grammar processing for a spoken language translation system
A problem with many speech understanding systems is that grammars that are more suitable for representing the relation between sentences and their meanings, such as context free grammars (CFGs) and augmented phrase structure grammars (APSGs), are computationally very demanding. On the other hand, finite state grammars are efficient, but cannot represent directly the sentence-meaning relation. The authors describe how speech recognition and language analysis can be tightly coupled by developing an APSG for the analysis component and deriving automatically from it a finite-state approximation that is used as the recognition language model. Using this technique, the authors have built an efficient translation system that is fast compared to others with comparably sized language models.<<ETX>>