一种新的基于瓶颈深度信念网络的特征提取方法及其在语种识别中的应用

New Feature Extraction Method Based on Bottleneck Deep Belief Networks and its Application in Language Recognition

在语种识别中,传统的MFCC特征由于每帧信号上的信息量不足,很容易受到噪声污染,且抗噪能力较弱。同时,目前普遍使用的SDC特征提取方法在参数选择上需要人为设定,这增加了识别结果的不确定性。针对上述不足,将深度学习方法引入特征提取之中,提出了基于瓶颈深度信念网络的特征提取方法。最后在NIST2007数据库上对瓶颈层的大小、隐层数目以及瓶颈层位置进行了相关的对比实验,结果表明,提出的方法相对于传统的特征提取方法能够取得更高的识别率。

In language recognition,due to the insufficiency of information in each frame,traditional MFCC feature extraction is easily suffered from noise pollution.Meanwhile,the general method of SDC feature extraction depends on artificially setting in parameter selection which increases the uncertainty of recognition performance.In order to overcome these drawbacks,the deep learning method was introduced and a novel feature extraction approach named BN-DBN which is based on deep learning was proposed.Finally,the relevant comparative experiments for the bottleneck layer size,the number of hidden layers and the position of the bottleneck layer were carried out in NIST2007 database.Experimental results show that extraction method of the bottleneck features based on deep belief networks are more effective in language recognition,compared with traditional methods.