关键词: 声学特征, 音素识别, 子空间高斯混合模型, 深度神经网络

Abstract: In order to reduce the phoneme recognition error rate of acoustic features in speech recognition system and improve system performance, a Subspace Gaussian Mixture Model（SGMM） and Deep Neural Network（DNN） combined with extraction features are proposed. The parameter size of SGMM is analyzed and the computational complexity is reduced. After the degree is connected with DNN, the phoneme recognition rate is further improved. The speech data transformed by nonlinear feature is input into the model to find the optimal configuration of the deep neural network structure, and a more reliable network model for learning and training is established for feature extraction. The phoneme recognition error rate is compared to judge the system performance. Experimental simulation results show that the features extracted based on the system are significantly better than the traditional acoustic model.

Key words: acoustic feature, phoneme recognition, subspace Gaussian mixture model, deep neural network