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

摘要： 为提高连续语音识别中的识别准确率，采用高斯伯努利受限玻尔兹曼机进行语音训练和识别。通过结合并行回火算法的思想，采样、交换不同的温度链下的重构数据，实现在全局范围内对整个分布进行采样，提出一种基于并行回火改进的高斯伯努利受限玻尔兹曼机（GRBM-PT）的建模方法。该方法通过对语音信号的连续数据进行预训练分析、建模，最后使用支持向量机作为语音识别的分类器。在TI-Digits数字语音训练和数字测试数据库上的实验结果表明，语音识别率能够达到83.14%，基于GRBM-PT模型下的语音识别率明显优于RBM，RBM-PT以及GRBM模型的性能。

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