连续音素的改进深信度网络的识别算法

Phoneme recognition based on deep belief network

为提高连续语音识别中的音素识别率,提出一种基于改进并行回火训练的受限波尔兹曼机的音素识别算法。首先,利用经过等能量划分后的改进并行回火算法来训练受限玻尔兹曼机,接着将受限玻尔兹曼机堆叠组成一个深信度网络,从而作为深度神经网络预训练的基础模型,然后通过softmax层输出,得到用于音素状态后验概率检测的深度神经网络。接着,利用少量的标签数据,根据反向传播算法对网络权重进行微调。最后,将所得后验概率作为隐马尔科夫的发射概率,然后利用Viterbi解码器实现音素识别。在TIMIT语料库上的实验表明,识别率相比于传统的对比散度类算法提高了约4.5%,在不增加计算量的情况下比原始并行回火算法提高约1%。

In order to improve the accuracy of phoneme recognition in continuous speech recognition,in this paper,a modified parallel tempering (PT) algorithm applied to train the restricted Boltzmann machine (RBM) is proposed.Firstly,RBM is trained in light of Metropolis-Hasting for parallel tempering sampling,then stacking up RBMs to form a deep belief network (DBN) as the basis for deep neural network (DNN) pre-training,then by adding an output layer called “softmax” to the network,a DNN detecting the posterior probability of phoneme can be created.Subsequently,backward propagation algorithm is applied to fine-tune the weights discriminatively with less label data.Finally,the sequence of the predicted probability distribution is fed into a standard Viterbi decoder.The experiments show that the proposed method has a better performance on the TIMIT dataset than traditional ways.Its recognition rate is higher 4.5% than contrastive divergence (CD),and 1% than original PT without more computation.