This feature vector made the Gaussian Mixture Model (GMM) classifier outperform MFCC and Differential MFCC features in classification.
该混合特征使得高斯混合模型(GMM)分类器可获得比使用MFCC特征及其差分MFCC更好的分类性能。
During the experiment, MFCC (Mel Frequency Ceptral Coefficient) is adopted to speaker speech feature parameters.
实验中，采用美尔倒谱系数(MFCC)作为话者语音特征参数。
The experiment results indicate that the new feature parameter WPP is able to outperform SBC and SBC is better than MFCC.
实验证明新特征参数WPP的语音识别性能优于SBC，而SBC的识别性能优于MFCC。
MFCC USES intermediate clustering results in one type of feature space to help the selection in other types of feature Spaces.
MFCC充分利用了一个特征空间的中间聚类结果来帮助另一个特征空间进行特征选择。
The MFCC coefficients and LPCC coefficients are combined as the speech recognition feature extraction parameters.
将梅尔倒谱参数和线性预测参数结合起来作为语音识别的特征提取参数。
The MFCC coefficients and LPCC coefficients are combined as the speech recognition feature extraction parameters.
将梅尔倒谱参数和线性预测参数结合起来作为语音识别的特征提取参数。
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