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 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.
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。
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.
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。
The MFCC coefficients and LPCC coefficients are combined as the speech recognition feature extraction parameters.
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