Context-free large-vocabulary connected speech recognition with evolutional grammars

The paper addresses the issue of how to efficiently incorporate a context free grammar into a large vocabulary speech recognizer while maintaining maximum recognizer performance. The new method uses an evolutional grammar (EG) model which produces finite state machines (FSMs) in real time or “on the fly” to accommodate context free grammatical rules. In addition, word models associated with those grammatical rules are built in real time. By optimizing a recursive transition network (RTN) before recognition, efficient instantaneous expansions of the grammatical rules can be ensured. Since all grammatical information is used at the same time that acoustical decoding is done, as opposed to application during a second “post processing” stage, the best attainable accuracy is ensured. Using these methods one can achieve 98% word accuracy and real-time grammatical decoding on the ARPA Naval Resource Management Task