基于滑动倒谱的自动语言辨识

滑动差分倒谱在自动语言辨识的研究中获得了广泛的应用. 但是滑动差分倒谱并没有利用语音信号的静态倒谱信息,在方言辨识中的研究表明静态倒谱比差分倒谱含有更多的特征信息. 为此,提出了滑动倒谱( SC)的概念, 并与滑动差分倒谱特征矢量进行了对比研究. 首先利用开发集的语音考察了滑动差分倒谱和滑动倒谱的控制参数在不同取值的情况下对识别性能的影响,利用爬山法确定了这2类特征矢量达到局部最优控制参数组合的路径,然后利用测试集的数据对优化后的2类特征矢量建立的模型进行了闭集辨识和开集辨识. 2种情况下的测试结果都表明滑动倒谱的性能优于滑动差分倒谱. 并且这2种参数还具有特征互补性,将它们进行决策级数据融合可以进一步提高系统的性能.

Automatic language identification using shifted cepstra

Shifted delta cep stra have been widely used in automatic language identification, but only delta cep strum information is emp loyed. Research on accent identification revealed that detailed cep strum ismore informative than delta cep strum. So shifted cep strum was p roposed and comparative studywas conducted between these two cep stra. Effects of their control parameters on recognition performance were investigated with speech data in the development set. The best paths of these two vectors to reach a locally op timal control parameter combination were determined with the hill2climbing method. Comparative tests performed with speech data both in the closed test set and open test set demonstrated that shifted cep stra is superior to shifted delta cep stra. In addition, they are mutually comp le2 mentary and data fusion at the decision level could further imp rove the performance of the system