关键词: 高斯伯努利受限玻尔兹曼机（GRBM）, 受限玻尔兹曼机, 并行回火, 语音识别

Abstract: To improve the performance of continuous data in speech recognition, the Gaussian-Bernoulli Restricted Boltzmann Machine（GRBM） is used to train and recognize the speech signal based on a developed recognition method. An improved GRBM network based on Parallel Tempering（GRBM-PT） is proposed by combining with the parallel tempering learning algorithm, which samples and swaps the reconstructed data in the different temperatures of entire distribution. Based on a scheme of pre-training and modeling the speech signal, the outputs are classified with a Support Vector Machine（SVM）. The experimental results of digit speech recognition on the core test of TI-Digits show that the proposed scheme works very well, the accuracy can be as high as 83.14%. It is found that the GRBM-PT performs better than other methods, such as RBM, RBM-PT and GRBM.

Key words: Gaussian-Bernoulli Restricted Boltzmann Machine（GRBM）, restricted Boltzmann machine, parallel tempering, speech recognition