【摘要】 针对语音识别中梅尔倒谱系数(MFCC)对中高频信号的识别精度不高,并且没有考虑各维特征参数对识别结果影响的问题,提出基于MFCC、逆梅尔倒谱系数(IMFCC)和中频梅尔倒谱系数(MidMFCC),并结合Fisher准则的特征提取方法。首先对语音信号提取MFCC、IMFCC和MidMFCC三种特征参数,分别计算三种特征参数中各维分量的Fisher比,通过Fisher比对三种特征参数进行选择,组成一种混合特征参数,提高语音中高频信息的识别精度。实验结果表明,在相同环境下,新的特征与MFCC参数相比,识别率有一定程度的提高。更多还原

【Abstract】 Concerning the low identification precision of Mel Frequency Cepstral Coefficients( MFCC) parameters in high frequency signals and the problem that the influence of each dimension feature parameters has not been considered to identify,the method of extracting features based on MFCC,IMFCC( Inverted MFCC) and MidMFCC( Mid-frequency MFCC) combined with Fisher criterion was adopted. Extracting MFCC,IMFCC and MidMFCC parameters from speech signals and calculating the Fisher ratio of components of three parameters,useful parameters were chosen by using Fisher standard and a mixture feature was constructed to improve mid-frequency and high frequency recognition accuracy. The experimental results show that the new feature has better recognition results compared with MFCC in the same environment.更多还原