系统提取的音频信号特征为线性预测美尔倒谱系数（LPCMCC），采用动态时间规整（DTW）的识别算法。
The audio signal feature, in this scheme, is the LPC Mel Cepstrum Coefficient (LPCMCC) and recognition algorithm is Dynamic Time Warping (DTW).
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.
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.
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