The experiment results show that the system built on DWT-MFC has better noise robustness than on LPCC and MFCC.

实验表明，与（LPCC）和（MFCC）参数相比，采用DWT-MFC参数使系统的噪声鲁棒性得到显著的提高。

The results of comparison prove that CDCC is better in classification, noise restraint and stability that LPCC.

实验证明这种混合倒谱系数具有较好的分类特性、抗噪性和稳定性。

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 weighted LPCC feature based on the distortion of speech coding was explored to reduce the influence under the matched condition.

在前者中，通过分析语音编码对LPCC参数的影响，提出了一种基于编码失真的LPCC加权参数。

By using full pole model, we obtained speech signal LPC, then deduced it's LPCC, and we used the LPCC difference to describe speaker's track dynamic movement.

通过应用全极点模型，提取语音信号的线性预测系数，并推导出其倒谱系数，获得线性预测倒谱差分，用以描述说话人声道的动态变化。

In this paper, we use full pole model to obtain speech signal LPC, then deduce it's LPCC, and we use the LPCC difference to describe speaker's track dynamic movement.

本文应用全极点模型，提取语音信号的线性预测系数，并推导出其倒谱系数，获得线性预测倒谱差分，用以描述说话人声道的动态变化。

In comparison under the same condition, the recognizing ratio of system that USES LPCMCC as character parameter of speech signal is better than that use of using LPCC.

相比较而言，在相同的条件下，LPCMCC作为语音信号的特征参数时，系统的识别率要高于LPCC作为语音信号特征参数的系统。