LR-parser-driven Viterbi search with hypotheses merging mechanism using context-dependent phone models

Describes a Viterbi search algorithm for continuous speech recognition using context-dependent phone models under the constraint defined by a context-free grammar (CFG). It is based on a frame-synchronous LR parser which dynamically generates a finite state network (FSN) from the CFG with an efficient path merging mechanism. Full context dependency (intra- and inter-word context) is taken into account in the likelihood calculation process. This paper first describes the algorithm and the processing mechanism, then compares the experimental results of our algorithm and the conventional tree-based HMM-LR speech recognition algorithm which uses HMMs and an LR parser in phone-synchronous processing. The experiments show that our algorithm runs faster than the conventional HMM-LR algorithm with an equivalent recognition accuracy.