Abstract: In order to further improve the robustness and recognition rate of bottleneck deep belief network in Large Vocabulary Continuous Speech Recognition system, this paper presented a novel bottleneck deep belief network to extract new features which was based on speaker adaptation and discriminative training. Firstly, a bottleneck deep belief network was adopted to get the feature，thus discriminative training performed on this basis which gave a more distinguished network to improve the recognition accuracy. Simultaneously, a more robust speaker adaptation method was introduced to adjust the network. The proposed method was tested on several public continuous speech databases with strong noise and casual themes and a relative 6.9% promotion of the recognition accuracy was obtained. The result proves the superiority of the proposed method compared to the conventional one.