隐马尔可夫模型的一种有区分力的反向传播训练方法

研究隐马尔可夫模型(HMM)的一种有区分力的训练方法.在多层前向神经网络的 框架中实现了HMM的前向概率计算.基于这一框架,利用偏导数的反向传播计算方法,通 过梯度上升的优化过程来实现互信息的最大化,从而对HMM进行有区分力的训练.这一 训练方法被称之为HMM的反向传播训练方法.此外,还设计了一个用以实现这一训练方 法的在数值计算上具有强鲁棒性的算法.语音识别的实验结果证实了这一训练方法的优越 性.

BACK-PROPAGATION APPROACH TO DISCRIMINATIVE TRAINING OF HIDDEN MARKOV MODEL

In this paper, an approach to the discriminative training of hidden Markov model (HMM) is presented. The forward probability calculation of HMM is accomplished within the framework of a multilayer feedforward neural network. Based on this framework, by making use of the back propagation method of computing partial derivatives, the maximization of mutual information can be achieved through the gradient ascent optimization process, and thus the discriminative training of HMM is performed. This approach to the training of HMM is called back propagation training approach. Additionally, a more numerically robust algorithm is also designed for implementing this approach. The superiority of this approach is proved by the results of the speech recognition experiments.