A continuous speech recognition model integrated by multi module RBF Gamma neural networks and HMMs is proposed in this paper.In this model

the abilities of RBF Gamma nets that effectively represent the space of speech units and synthesize the temporal correlation information of speech sequence are combined with the abilities of HMMs that integrate and expand the speech units in time domain.They are mutually complementary in function and improve the recognition accuracy obviously.A speaker dependent continuous digits recognition system is realized according to this model and using the learning algorithms proposed in this paper for improving classification performance.The tested digit accuracy is 98.9% and the string accuracy is 94.8%.