The control of continuous speech recognition by a context-free based language model requires a parsing process which may overload the acoustic decoding algorithm. We present a new approach to integrate such a language model in the search process. This approach extends the beam search Viterbi algorithm. In our case, the pruning technique not only selects the most likely acoustic hypotheses but also governs the dynamic expansion of a network structure. This algorithm is general enough to cope with the self-embedded recursivity of context-free languages and it favourably compares with other parsing techniques applied to spoken inputs. We present results which show that the syntactical knowledge may be efficiently included at the frame level of an acoustic decoding algorithm.

Keywords: Continuous Speech Recognition, Context-Free Language Models, Beam Search Viterbi Algorithm