Acceleration Strategies for Speech Recognition Based on Deep Neural Networks

Abstract

The Context-Dependent Deep-Neural-Network HMM, or CD-DNN-HMM, is a powerful acoustic modeling technique for HMM-based speech recognition systems. The CD-DNN-HMM can greatly outperform against the conventional Gaussian-mixture HMMs. Therefore, we build a CD-DNN-HMM LVCSR system by modifying a mature GMM-HMM system. The baseline CD-DNN-HMM system achieve word-error rate of 18.6% that is far better than 24.9% achieved by the GMM-HMM system. However, the speed of the baseline CD-DNN-HMM system becomes a major roadblock for its real-time rate reaches 0.72 on the standard NIST 2000 Hub5 evaluation set. In this paper, we realize several optimization algorithms in our baseline system to accelerate the recognition speed. Testing the optimized system on the same evaluation set, we achieve real-time rate of 0.39, a relative reduction of 45.8%.